Publications

2025

1. Preprint

   A Dataset-free Approach for Self-supervised Learning of 3D Reflectional Symmetries

   I. Aguirre, I. Sipiran, and G. Montañana

   arXiv preprint arXiv:2503.02660, , Mar 2025

   Abs DOI Bib PUBLICATION

   In this paper, we explore a self-supervised model that learns to detect the symmetry of a single object without requiring a dataset—relying solely on the input object itself. We hypothesize that the symmetry of an object can be determined by its intrinsic features, eliminating the need for large datasets during training. Additionally, we design a self-supervised learning strategy that removes the necessity of ground truth labels.

   @article{Aguirre2025DatasetFree,
     title = {A Dataset-free Approach for Self-supervised Learning of 3D Reflectional Symmetries},
     author = {Aguirre, I. and Sipiran, I. and Montañana, G.},
     journal = {arXiv preprint arXiv:2503.02660},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2025},
     month = mar,
     publisher = {},
     doi = {},
   }

2. ESWA

   Multi-Label Learning on Low Label Density Sets with Few Examples

   M. Vergara, B. Bustos, I. Sipiran, T. Schreck, and S. Lengauer

   Expert Systems With Applications, , 2025

   Abs DOI Bib PUBLICATION

   We present a problem for multi-label learning in few examples with low label density. We propose a pipeline to achieve effective classifications on the ...

   @article{Vergara2025MultiLabel,
     title = {Multi-Label Learning on Low Label Density Sets with Few Examples},
     author = {Vergara, M. and Bustos, B. and Sipiran, I. and Schreck, T. and Lengauer, S.},
     journal = {Expert Systems With Applications},
     location = {},
     volume = {265},
     issue = {},
     pages = {125942},
     numpages = {},
     year = {2025},
     month = {},
     publisher = {Elsevier},
     doi = {10.1016/j.eswa.2024.125942},
   }

3. IEEE Access

   Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery using Deep Transfer Learning and Active Contour Loss

   M. Chicchón, F. Leon-Trujillo, I. Sipiran, and R. Madrid

   IEEE Access, , 2025

   Abs DOI Bib PUBLICATION

   An accurate land-cover segmentation of very-high-resolution aerial images is essential for a wide range of applications, including urban planning and natural resource management. However, the automation of this process remains a challenge owing to the complexity of images, variability in land surface features, and noise. In this study, a method for training convolutional neural networks and transformers to perform land-cover segmentation on very-high-resolution aerial images in a regional context was proposed. We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. Our experiments conducted using the OpenEarthMap dataset, which includes images from 44 countries, demonstrate the superior performance of U-Net-scSE models with the EfficientNet-V2-XL and MiT-B4 encoders, achieving an mIoU of over 0.80 on a test dataset of urban and rural images from Peru.

   @article{Chicchon2025LandCover,
     title = {Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery using Deep Transfer Learning and Active Contour Loss},
     author = {Chicchón, M. and Leon-Trujillo, F. and Sipiran, I. and Madrid, R.},
     journal = {IEEE Access},
     location = {},
     volume = {13},
     issue = {},
     pages = {59007--59019},
     numpages = {},
     year = {2025},
     month = {},
     publisher = {IEEE},
     doi = {10.1109/ACCESS.2025.3556632},
   }

2024

1. CACM

   The Role of Computing in the Study of Latin American Cultural Heritage

   I. Sipiran

   Communications of the ACM, New York, Aug 2024

   Abs DOI Bib PUBLICATION

   In this article, we present some of the recent efforts to build state-of-the-art computational technology to preserve and conserve cultural heritage in Latin America.

   @article{Sipiran2024RoleComputing,
     title = {The Role of Computing in the Study of Latin American Cultural Heritage},
     author = {Sipiran, I.},
     journal = {Communications of the ACM},
     location = {New York},
     volume = {67},
     issue = {8},
     pages = {58--62},
     numpages = {},
     year = {2024},
     month = aug,
     publisher = {ACM},
     doi = {10.1145/3653309},
   }

2. VisComp

   A Convolutional Architecture for 3D Model Embedding Using Image Views

   A. Labrada, B. Bustos, and I. Sipiran

   The Visual Computer, , 2024

   Abs DOI Bib PUBLICATION

   We propose a deep learning architecture to handle 3D models represented as sets of image views as input. Our proposed architecture combines ...

   @article{Labrada2024Convolutional3D,
     title = {A Convolutional Architecture for 3D Model Embedding Using Image Views},
     author = {Labrada, A. and Bustos, B. and Sipiran, I.},
     journal = {The Visual Computer},
     location = {},
     volume = {40},
     issue = {},
     pages = {1601--1615},
     numpages = {},
     year = {2024},
     month = {},
     publisher = {Springer},
     doi = {10.1007/s00371-023-02872-4},
   }

3. JOCCH

   Repetitive Patterns Recognition in Textures of Ancient Peruvian Pottery

   S. Sepulveda, B. Bustos, and I. Sipiran

   ACM Journal on Computing and Cultural Heritage, , 2024

   Abs DOI Bib PUBLICATION

   We present a study and comparison of computer vision methods for the task of finding repetitive motifs in ancient Peruvian pottery. Under this context, the main difficulties for solving the task are that the motifs are in most cases highly repetitive, that the motifs corresponding to the same pattern are slightly different due to being hand-drawn, and that the amount of data available for training and testing purposes is scarce.

   @article{Sepulveda2024RepetitivePatterns,
     title = {Repetitive Patterns Recognition in Textures of Ancient Peruvian Pottery},
     author = {Sepulveda, S. and Bustos, B. and Sipiran, I.},
     journal = {ACM Journal on Computing and Cultural Heritage},
     location = {},
     volume = {17},
     issue = {4},
     pages = {55:1--55:16},
     numpages = {},
     year = {2024},
     month = {},
     publisher = {ACM},
     doi = {10.1145/3694789},
   }

4. ECCV

   Unsupervised Video Summarization: A Reconstruction Model with Proximal Gradient Methods

   A. Alfaro and I. Sipiran

   In ECCV Workshop on Traditional Computer Vision, , 2024

   Abs DOI Bib PUBLICATION

   We propose an unsupervised video summarization method by motion-based frame selection and a novel clustering validity index to determine the optimal representatives of the original video.

   @inproceedings{Alfaro2024UnsupervisedVideo,
     title = {Unsupervised Video Summarization: A Reconstruction Model with Proximal Gradient Methods},
     author = {Alfaro, A. and Sipiran, I.},
     booktitle = {ECCV Workshop on Traditional Computer Vision},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2024},
     month = {},
     publisher = {},
     doi = {},
   }

5. VISART

   Cultural Heritage 3D Reconstruction with Diffusion Networks

   P. Jaramillo and I. Sipiran

   In ECCV Workshop on Vision for Art (VISART VII), , 2024

   Abs DOI Bib PUBLICATION

   This article explores the use of recent generative AI algorithms for repairing cultural heritage objects, leveraging a conditional diffusion model designed to reconstruct 3D point clouds effectively. Our study evaluates the model’s performance across general and cultural heritage-specific settings. Results indicate that, with considerations for object variability, the diffusion model can accurately reproduce cultural heritage geometries.

   @inproceedings{Jaramillo2024CulturalHeritage,
     title = {Cultural Heritage 3D Reconstruction with Diffusion Networks},
     author = {Jaramillo, P. and Sipiran, I.},
     booktitle = {ECCV Workshop on Vision for Art (VISART VII)},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2024},
     month = {},
     publisher = {},
     doi = {},
   }

6. 3DOR

   Self-distillation for Efficient Object-level Point Cloud Learning

   Lucas Oyarzún, Iván Sipiran, and José M. Saavedra

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , 2024

   Abs DOI Bib PUBLICATION

   This study investigates the application of self-distillation techniques based on Siamese networks, BYOL, and SIMSIAM, to pre-train encoders designed for 3D point cloud processing.

   @inproceedings{Oyarzun2024SelfDistillation,
     title = {Self-distillation for Efficient Object-level Point Cloud Learning},
     author = {Oyarzún, Lucas and Sipiran, Iván and Saavedra, José M.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2024},
     month = {},
     publisher = {},
     doi = {},
   }

2023

1. AutCon

   Large-scale Multi-unit Floor Plan Dataset for Architectural Plan Analysis and Recognition

   Pablo N. Pizarro, Nancy Hitschfeld, and Iván Sipiran

   Automation in Construction, , Dec 2023

   Abs DOI Bib PUBLICATION

   This paper presents a multi-unit floor plan dataset comprising 954 high-resolution images of residential buildings with annotated walls and slabs as polygons, enabling large-scale plan analysis.

   @article{Pizarro2023FloorPlanDataset,
     title = {Large-scale Multi-unit Floor Plan Dataset for Architectural Plan Analysis and Recognition},
     author = {Pizarro, Pablo N. and Hitschfeld, Nancy and Sipiran, Iván},
     journal = {Automation in Construction},
     location = {},
     volume = {156},
     issue = {},
     pages = {105132},
     numpages = {},
     year = {2023},
     month = dec,
     publisher = {Elsevier},
     doi = {10.1016/j.autcon.2023.105132},
   }

2. IEEE Access

   Semantic Segmentation of Fish and Underwater Environments Using Deep Convolutional Neural Networks and Learned Active Contours

   Miguel Chicchón, Héctor Bedón, Carlos R. Del-Blanco, and Iván Sipiran

   IEEE Access, , 2023

   Abs DOI Bib PUBLICATION

   This study proposes a combined loss function based on active contour theory and level set methods to refine the spatial segmentation resolution and quality in underwater environments.

   @article{Chicchon2023UnderwaterSegmentation,
     title = {Semantic Segmentation of Fish and Underwater Environments Using Deep Convolutional Neural Networks and Learned Active Contours},
     author = {Chicchón, Miguel and Bedón, Héctor and Del-Blanco, Carlos R. and Sipiran, Iván},
     journal = {IEEE Access},
     location = {},
     volume = {11},
     issue = {},
     pages = {33652--33665},
     numpages = {},
     year = {2023},
     month = {},
     publisher = {IEEE},
     doi = {},
   }

3. 3DOR

   SHREC 2023: Detection of Symmetries on 3D Point Clouds Representing Simple Shapes

   Iván Sipiran, Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti, Gerasimos Arvanitis, and 11 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , 2023

   Abs DOI Bib PUBLICATION

   This paper presents the methods that participated in the SHREC 2023 track focused on detecting symmetries on 3D point clouds representing simple shapes.

   @inproceedings{Sipiran2023SHREC,
     title = {SHREC 2023: Detection of Symmetries on 3D Point Clouds Representing Simple Shapes},
     author = {Sipiran, Iván and Romanengo, Chiara and Falcidieno, Bianca and Biasotti, Silvia and Arvanitis, Gerasimos and Chen, Chen and Fotis, Vlassis and He, Jianfang and Lv, Xiaoling and Moustakas, Konstantinos and Peng, Silong and Romanelis, Ioannis and Sun, Wenhao and Vlachos, Christoforos and Wu, Ziyu and Xie, Qiong},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2023},
     month = {},
     publisher = {},
     doi = {},
   }

4. JWSCG

   Visual Exploration of Repetitive Patterns on Ancient Peruvian Pottery

   Stefan Lengauer, Lin Shao, Magdalena Mayerhofer, Reinhold Preiner, Stephan Karl, and 4 more authors

   Journal of WSCG, , Nov 2023

   Abs DOI Bib PUBLICATION

   This study presents an approach for the interactive visual exploration and correlation of shape-and ornament-based properties of a large collection of ancient vessels.

   @article{Lengauer2023VisualExploration,
     title = {Visual Exploration of Repetitive Patterns on Ancient Peruvian Pottery},
     author = {Lengauer, Stefan and Shao, Lin and Mayerhofer, Magdalena and Preiner, Reinhold and Karl, Stephan and Trinkl, Elisabeth and Sipiran, Iván and Bustos, Benjamin and Schreck, Tobias},
     journal = {Journal of WSCG},
     location = {},
     volume = {31},
     issue = {1-2},
     pages = {25--33},
     numpages = {},
     year = {2023},
     month = nov,
     publisher = {},
     doi = {10.24132/JWSCG.2023.3},
   }

5. ICCVW

   MatchMakerNet: Enabling Fragment Matching for Cultural Heritage Analysis

   Ariana M. Villegas-Suarez, Cristian Lopez, and Iván Sipiran

   In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), , 2023

   Abs DOI Bib PUBLICATION

   This paper proposes MatchMakerNet, a network architecture designed to automate the pairing of object fragments for reassembly in cultural heritage preservation.

   @inproceedings{Villegas2023MatchMakerNet,
     title = {MatchMakerNet: Enabling Fragment Matching for Cultural Heritage Analysis},
     author = {Villegas-Suarez, Ariana M. and Lopez, Cristian and Sipiran, Iván},
     booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2023},
     month = {},
     publisher = {},
     doi = {},
   }

6. ICCVW

   Evaluation of 3D Reconstruction for Cultural Heritage Applications

   Cristián Llull, Nelson Baloian, Benjamin Bustos, Kornelius Kupczik, Iván Sipiran, and 1 more author

   In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), , Oct 2023

   Abs DOI Bib PUBLICATION

   This paper investigates the principles and application of the Chamfer distance for evaluating reconstructions produced by different methods: Photogrammetry, NeRF, and NVDiffrec.

   @inproceedings{Llull2023Evaluation3DReconstruction,
     title = {Evaluation of 3D Reconstruction for Cultural Heritage Applications},
     author = {Llull, Cristián and Baloian, Nelson and Bustos, Benjamin and Kupczik, Kornelius and Sipiran, Iván and Baloian, Andrés},
     booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
     location = {},
     volume = {},
     issue = {},
     pages = {1634--1643},
     numpages = {},
     year = {2023},
     month = oct,
     publisher = {IEEE},
     doi = {10.1109/ICCVW60793.2023.00179},
   }

2022

1. IJCV

   Data-Driven Restoration of Digital Archaeological Pottery with Point Cloud Analysis

   Iván Sipiran, Alexis Mendoza, Alexander Apaza, and Cristian Lopez

   International Journal of Computer Vision, , Sep 2022

   Abs DOI Bib PUBLICATION

   This paper proposes a method to repair the digital objects’ surface using a data-driven approach. A point cloud neural network is designed and trained to synthesize the missing geometry in an end-to-end manner.

   @article{Sipiran2022DataDrivenRestoration,
     title = {Data-Driven Restoration of Digital Archaeological Pottery with Point Cloud Analysis},
     author = {Sipiran, Iván and Mendoza, Alexis and Apaza, Alexander and Lopez, Cristian},
     journal = {International Journal of Computer Vision},
     location = {},
     volume = {130},
     issue = {9},
     pages = {2149--2165},
     numpages = {},
     year = {2022},
     month = sep,
     publisher = {Springer},
     doi = {10.1007/s11263-022-01637-1},
   }

2. CompGraph

   SHREC 2022: Fitting and Recognition of Simple Geometric Primitives on Point Clouds

   Chiara Romanengo, Andrea Raffo, Silvia Biasotti, Bianca Falcidieno, Vlassis Fotis, and 15 more authors

   Computers & Graphics, , Oct 2022

   Abs DOI Bib PUBLICATION

   This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds.

   @article{Romanengo2022SHREC,
     title = {SHREC 2022: Fitting and Recognition of Simple Geometric Primitives on Point Clouds},
     author = {Romanengo, Chiara and Raffo, Andrea and Biasotti, Silvia and Falcidieno, Bianca and Fotis, Vlassis and Romanelis, Ioannis and Psatha, Eleftheria and Moustakas, Konstantinos and Sipiran, Iván and Nguyen, Quang-Thuc and Chu, Chi-Bien and Nguyen-Ngoc, Khoi and Vo, Dinh-Khoi and To, Tuan-An and Nguyen, Nham-Tan and Le-Pham, Nhat-Quynh and Nguyen, Hai-Dang and Tran, Minh-Triet and Qie, Yifan and Anwer, Nabil},
     journal = {Computers & Graphics},
     location = {},
     volume = {107},
     issue = {},
     pages = {32--49},
     numpages = {},
     year = {2022},
     month = oct,
     publisher = {Elsevier},
     doi = {10.1016/j.cag.2022.07.004},
   }

3. CompGraph

   SHREC 2022: Pothole and Crack Detection in the Road Pavement Using Images and RGB-D Data

   Elia Moscoso Thompson, Andrea Ranieri, Silvia Biasotti, Miguel Chicchón, Iván Sipiran, and 4 more authors

   Computers & Graphics, , Oct 2022

   Abs DOI Bib PUBLICATION

   This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement using images and RGB-D data.

   @article{Thompson2022SHREC,
     title = {SHREC 2022: Pothole and Crack Detection in the Road Pavement Using Images and RGB-D Data},
     author = {Thompson, Elia Moscoso and Ranieri, Andrea and Biasotti, Silvia and Chicchón, Miguel and Sipiran, Iván and Pham, Minh-Khoi and Nguyen-Ho, Thang-Long and Nguyen, Hai-Dang and Tran, Minh-Triet},
     journal = {Computers & Graphics},
     location = {},
     volume = {107},
     issue = {},
     pages = {161--171},
     numpages = {},
     year = {2022},
     month = oct,
     publisher = {Elsevier},
     doi = {10.1016/j.cag.2022.07.018},
   }

4. AutCon

   Automatic Floor Plan Analysis and Recognition

   Pablo N. Pizarro, Nancy Hitschfeld, Iván Sipiran, and José M. Saavedra

   Automation in Construction, , Aug 2022

   Abs DOI Bib PUBLICATION

   This paper provides a critical revision of the methodologies and tools from rule-based and learning-based approaches for automatic floor plan analysis and recognition.

   @article{Pizarro2022FloorPlan,
     title = {Automatic Floor Plan Analysis and Recognition},
     author = {Pizarro, Pablo N. and Hitschfeld, Nancy and Sipiran, Iván and Saavedra, José M.},
     journal = {Automation in Construction},
     location = {},
     volume = {140},
     issue = {},
     pages = {104348},
     numpages = {},
     year = {2022},
     month = aug,
     publisher = {Elsevier},
     doi = {10.1016/j.autcon.2022.104348},
   }

5. AIRev

   A Comprehensive Review of the Video-to-Text Problem

   Jesús Perez-Martin, Benjamin Bustos, Silvio Jamil F. Guimarães, Iván Sipiran, Jorge Pérez, and 1 more author

   Artificial Intelligence Review, , May 2022

   Abs DOI Bib PUBLICATION

   This paper reviews the video-to-text problem, in which the goal is to associate an input video with its textual description, covering state-of-the-art techniques and benchmark datasets.

   @article{PerezMartin2022VideoToText,
     title = {A Comprehensive Review of the Video-to-Text Problem},
     author = {Perez-Martin, Jesús and Bustos, Benjamin and Guimarães, Silvio Jamil F. and Sipiran, Iván and Pérez, Jorge and Coello, Grethel},
     journal = {Artificial Intelligence Review},
     location = {},
     volume = {55},
     issue = {5},
     pages = {4165--4239},
     numpages = {},
     year = {2022},
     month = may,
     publisher = {Springer},
     doi = {10.1007/s10462-021-10104-1},
   }

6. IT

   Interactive Annotation of Geometric Ornamentation on Painted Pottery Assisted by Deep Learning

   Stefan Lengauer, Peter Houska, Reinhold Preiner, Elisabeth Trinkl, Stephan Karl, and 3 more authors

   it - Information Technology, , Aug 2022

   Abs DOI Bib PUBLICATION

   This paper presents a system for the interactive annotation of geometric ornamentation on painted pottery, assisted by deep learning techniques.

   @article{Lengauer2022InteractiveAnnotation,
     title = {Interactive Annotation of Geometric Ornamentation on Painted Pottery Assisted by Deep Learning},
     author = {Lengauer, Stefan and Houska, Peter and Preiner, Reinhold and Trinkl, Elisabeth and Karl, Stephan and Sipiran, Iván and Bustos, Benjamin and Schreck, Tobias},
     journal = {it - Information Technology},
     location = {},
     volume = {64},
     issue = {6},
     pages = {217--231},
     numpages = {},
     year = {2022},
     month = aug,
     publisher = {De Gruyter},
     doi = {10.1515/itit-2022-0007},
   }

7. GCH

   Context-Based Surface Pattern Completion of Ancient Pottery

   Stefan Lengauer, Reinhold Preiner, Iván Sipiran, Stephan Karl, Elisabeth Trinkl, and 2 more authors

   In Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage, , 2022

   Abs DOI Bib PUBLICATION

   This paper presents an approach for context-based surface pattern completion of ancient pottery, enabling the reconstruction of repetitive patterns on fragmented artifacts.

   @inproceedings{Lengauer2022ContextBased,
     title = {Context-Based Surface Pattern Completion of Ancient Pottery},
     author = {Lengauer, Stefan and Preiner, Reinhold and Sipiran, Iván and Karl, Stephan and Trinkl, Elisabeth and Bustos, Benjamin and Schreck, Tobias},
     booktitle = {Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage},
     location = {},
     volume = {},
     issue = {},
     pages = {107--115},
     numpages = {},
     year = {2022},
     month = {},
     publisher = {Eurographics Association},
     doi = {10.2312/gch.20221234},
   }

2021

1. CompGraph

   SHREC 2021: Retrieval of Cultural Heritage Objects

   Iván Sipiran, Patrick Lazo, Cristian Lopez, Mauricio Jimenez, Nikhil Bagewadi, and 21 more authors

   Computers & Graphics, , Nov 2021

   Abs DOI Bib PUBLICATION

   This paper presents the methods and results of the SHREC 2021 track on the retrieval of cultural heritage objects, focusing on shape and cultural features.

   @article{Sipiran2021SHREC,
     title = {SHREC 2021: Retrieval of Cultural Heritage Objects},
     author = {Sipiran, Iván and Lazo, Patrick and Lopez, Cristian and Jimenez, Mauricio and Bagewadi, Nikhil and Bustos, Benjamin and Dao, Huy and Gangisetty, Suman and Hanik, Marek and Ho-Thi, Nguyen-Phuong and Holenderski, Maciej and Jarnikov, Dmitri and Labrada, Andrés and Licandro, Rodrigo and Nguyen, Dinh-Hoang and Nguyen-Ho, Thang-Long and Perez-Rey, Luis A. and Pham, Binh-Dang and Pham, Minh-Khoi and Preiner, Reinhold and Schreck, Tobias and Trinh, Quang-Huy and Tonnaer, Laurens and von Tycowicz, Christoph and Vu-Le, Tuan-Anh and Lengauer, Stefan},
     journal = {Computers & Graphics},
     location = {},
     volume = {100},
     issue = {},
     pages = {1--20},
     numpages = {},
     year = {2021},
     month = nov,
     publisher = {Elsevier},
     doi = {10.1016/j.cag.2021.07.010},
   }

2. CGF

   A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces

   Stefan Lengauer, Iván Sipiran, Reinhold Preiner, Tobias Schreck, and Benjamin Bustos

   Computer Graphics Forum, , 2021

   Abs DOI Bib PUBLICATION

   This paper presents an annotated dataset of 82 different 3D models of painted ancient Peruvian vessels, exhibiting different levels of repetitiveness in their surface patterns.

   @article{Lengauer2021BenchmarkDataset,
     title = {A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces},
     author = {Lengauer, Stefan and Sipiran, Iván and Preiner, Reinhold and Schreck, Tobias and Bustos, Benjamin},
     journal = {Computer Graphics Forum},
     location = {},
     volume = {40},
     issue = {5},
     pages = {},
     numpages = {},
     year = {2021},
     month = {},
     publisher = {Wiley},
     doi = {10.1111/cgf.14352},
   }

2020

1. CompGraph

   SHREC’20 Track: Retrieval of Digital Surfaces with Similar Geometric Reliefs

   E. Moscoso, S. Biasotti, A. Giachetti, C. Tortorici, N. Werghi, and 21 more authors

   Computers & Graphics, , Oct 2020

   Abs DOI Bib PUBLICATION

   This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge.

   @article{Moscoso2020SHREC,
     title = {SHREC’20 Track: Retrieval of Digital Surfaces with Similar Geometric Reliefs},
     author = {Moscoso, E. and Biasotti, S. and Giachetti, A. and Tortorici, C. and Werghi, N. and Shaker, A. and Berretti, S. and Nguyen-Dinh, H. and Le, M. and Nguyen, H. and Tran, M. and Gigli, L. and Velasco-Forero, S. and Marcotegui, B. and Sipiran, I. and Bustos, B. and Romanelis, I. and Fotis, V. and Arvanitis, G. and Moustakas, K. and Otu, E. and Zwiggelaar, R. and Hunter, D. and Liu, Y. and Arteaga, Y. and Luxman, R.},
     journal = {Computers & Graphics},
     location = {},
     volume = {91},
     issue = {},
     pages = {199--218},
     numpages = {},
     year = {2020},
     month = oct,
     publisher = {Elsevier},
     doi = {10.1016/j.cag.2020.07.005},
   }

2. 3DImaging

   3D Shape Matching for Retrieval and Recognition

   Benjamin Bustos and Iván Sipiran

   In 3D Imaging, Analysis and Applications, , 2020

   Abs DOI Bib PUBLICATION

   This chapter gives an overview of 3D shape matching and its applications to 3D shape retrieval and 3D shape recognition.

   @incollection{Bustos2020ShapeMatching,
     title = {3D Shape Matching for Retrieval and Recognition},
     author = {Bustos, Benjamin and Sipiran, Iván},
     booktitle = {3D Imaging, Analysis and Applications},
     location = {},
     volume = {},
     issue = {},
     pages = {413--461},
     numpages = {},
     year = {2020},
     month = {},
     publisher = {Springer},
     doi = {10.1007/978-3-030-44070-1_9},
   }

3. GCH

   Semi-automated Annotation of Repetitive Ornaments on 3D Painted Pottery Surfaces

   Stefan Lengauer, Alexander Komar, Stephan Karl, Elisabeth Trinkl, Iván Sipiran, and 2 more authors

   In Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage, , Nov 2020

   Abs DOI Bib PUBLICATION

   We propose a tool supporting the creation of archaeological drawings with a semi-automatic extraction of ornamental surface sections, based on a combination of user-defined queries and self-similarity detection.

   @inproceedings{Lengauer2020Annotation,
     title = {Semi-automated Annotation of Repetitive Ornaments on 3D Painted Pottery Surfaces},
     author = {Lengauer, Stefan and Komar, Alexander and Karl, Stephan and Trinkl, Elisabeth and Sipiran, Iván and Schreck, Tobias and Preiner, Reinhold},
     booktitle = {Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2020},
     month = nov,
     publisher = {Eurographics Association},
     doi = {10.2312/gch.20201285},
   }

2018

1. 3DOR

   Completion of Cultural Heritage Objects with Rotational Symmetry

   Iván Sipiran

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , Apr 2018

   Abs DOI Bib PUBLICATION

   We present a framework to complete 3D objects that exhibit rotational symmetry, combining algorithms from symmetry detection and surface reconstruction.

   @inproceedings{Sipiran2018Completion,
     title = {Completion of Cultural Heritage Objects with Rotational Symmetry},
     author = {Sipiran, Iván},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     volume = {},
     issue = {},
     pages = {87--93},
     numpages = {},
     year = {2018},
     month = apr,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20182004},
   }

2. CGI

   3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network

   Renato Hermoza and Iván Sipiran

   In Proceedings of Computer Graphics International (CGI), , Jun 2018

   Abs DOI Bib PUBLICATION

   We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN).

   @inproceedings{Hermoza2018GAN,
     title = {3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network},
     author = {Hermoza, Renato and Sipiran, Iván},
     booktitle = {Proceedings of Computer Graphics International (CGI)},
     location = {},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2018},
     month = jun,
     publisher = {ACM},
     doi = {10.1145/3208159.3208173},
   }

2017

1. VisComp

   Scalable 3D Shape Retrieval Using Local Features and the Signature Quadratic Form Distance

   I. Sipiran, J. Lokoc, B. Bustos, and T. Skopal

   The Visual Computer, , 2017

   Abs DOI Bib PUBLICATION

   We present a scalable and unsupervised approach for content-based retrieval on 3D model collections. Our goal is to represent a 3D shape as a set of discriminative local features, which is important to maintain robustness against deformations such as non-rigid transformations and partial data. However, this representation brings up the problem on how to compare two 3D models represented by feature sets. For solving this problem, we apply the signature quadratic form distance (SQFD), which is suitable for comparing feature sets.

   @article{Sipiran2017Scalable3D,
     title = {Scalable 3D Shape Retrieval Using Local Features and the Signature Quadratic Form Distance},
     author = {Sipiran, I. and Lokoc, J. and Bustos, B. and Skopal, T.},
     journal = {The Visual Computer},
     location = {},
     volume = {33},
     issue = {12},
     pages = {1571--1585},
     numpages = {},
     year = {2017},
     month = {},
     publisher = {Springer},
     doi = {10.1007/s00371-016-1301-5},
   }

2. JOCCH

   From Reassembly to Object Completion - A Complete Systems Pipeline

   G. Papaioannou, T. Schreck, A. Andreadis, P. Mavridis, R. Gregor, and 2 more authors

   ACM Journal on Computing and Cultural Heritage, , Feb 2017

   Abs DOI Bib PUBLICATION

   The problem of the restoration of broken artifacts, where large parts could be missing, is of high importance in archaeology. The typical manual restoration can become a tedious and error-prone process, which also does not scale well. In recent years, many methods have been proposed for assisting the process, most of which target specialized object types or operate under very strict constraints.

   @article{Papaioannou2017Reassembly,
     title = {From Reassembly to Object Completion - A Complete Systems Pipeline},
     author = {Papaioannou, G. and Schreck, T. and Andreadis, A. and Mavridis, P. and Gregor, R. and Sipiran, I. and Vardis, K.},
     journal = {ACM Journal on Computing and Cultural Heritage},
     location = {},
     volume = {10},
     issue = {2},
     pages = {8:1--8:22},
     numpages = {},
     year = {2017},
     month = feb,
     publisher = {ACM},
     doi = {10.1145/3009905},
   }

3. ICCVW

   Analysis of Partial Axial Symmetry on 3D Surfaces and Its Application in the Restoration of Cultural Heritage Objects

   I. Sipiran

   In Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), , Oct 2017

   Abs DOI Bib PUBLICATION

   We address the problem of finding the partial axial symmetry of 3D objects through the analysis of surface descriptors with invariance to transformations.

   @inproceedings{Sipiran2017PartialSymmetry,
     title = {Analysis of Partial Axial Symmetry on 3D Surfaces and Its Application in the Restoration of Cultural Heritage Objects},
     author = {Sipiran, I.},
     booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW)},
     location = {},
     volume = {},
     issue = {},
     pages = {2925--2933},
     numpages = {},
     year = {2017},
     month = oct,
     publisher = {IEEE},
     doi = {10.1109/ICCVW.2017.343},
   }

4. 3DOR

   SHREC’17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys

   F. A. Limberger, R. C. Wilson, M. Aono, N. Audebert, A. Boulch, and 13 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , Apr 2017

   Abs DOI Bib PUBLICATION

   The aim of this track is to create a fair benchmark to evaluate the performance of methods on the non-rigid point-cloud shape retrieval problem.

   @inproceedings{Limberger2017SHREC,
     title = {SHREC'17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys},
     author = {Limberger, F. A. and Wilson, R. C. and Aono, M. and Audebert, N. and Boulch, A. and Bustos, B. and Giachetti, A. and Godil, A. and Le Saux, B. and Li, B. and Lu, Y. and Nguyen, H. D. and Nguyen, V. T. and Pham, V. K. and Sipiran, I. and Tatsuma, A. and Tran, M. T. and Velasco-Forero, S.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     volume = {},
     issue = {},
     pages = {75--84},
     numpages = {},
     year = {2017},
     month = apr,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20171056},
   }

5. 3DOR

   SHREC’17 Track: Retrieval of Surfaces with Similar Relief Patterns

   S. Biasotti, E. Moscoso Thompson, M. Aono, A. Ben Hamza, B. Bustos, and 17 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , Apr 2017

   Abs DOI Bib PUBLICATION

   This paper presents the results of the SHREC’17 contest on retrieval of surfaces with similar relief patterns. The proposed task was created in order to verify the possibility of retrieving surface patches with a relief pattern similar to an example from a database of small surface elements.

   @inproceedings{Biasotti2017SHREC,
     title = {SHREC'17 Track: Retrieval of Surfaces with Similar Relief Patterns},
     author = {Biasotti, S. and Moscoso Thompson, E. and Aono, M. and Ben Hamza, A. and Bustos, B. and Dong, S. and Du, B. and Fehri, A. and Li, H. and Limberger, F. A. and Masoumi, M. and Rezaei, M. and Sipiran, I. and Sun, L. and Tatsuma, A. and Velasco Forero, S. and Wilson, R. C. and Wu, Y. and Zhang, J. and Zhao, T. and Fornasa, F. and Giachetti, A.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     volume = {},
     issue = {},
     pages = {95--103},
     numpages = {},
     year = {2017},
     month = apr,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20171058},
   }

2016

1. JVLC

   Guiding the Exploration of Scatter Plot Data Using Motif-Based Interest Measures

   L. Shao, T. Schleicher, M. Behrisch, T. Schreck, I. Sipiran, and 1 more author

   Journal of Visual Languages and Computing, , 2016

   Abs DOI Bib PUBLICATION

   We propose a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments.

   @article{Shao2016ScatterPlot,
     title = {Guiding the Exploration of Scatter Plot Data Using Motif-Based Interest Measures},
     author = {Shao, L. and Schleicher, T. and Behrisch, M. and Schreck, T. and Sipiran, I. and Keim, D. A.},
     journal = {Journal of Visual Languages and Computing},
     location = {},
     volume = {36},
     issue = {},
     pages = {1--12},
     numpages = {},
     year = {2016},
     month = {},
     publisher = {Elsevier},
     doi = {10.1016/j.jvlc.2016.07.003},
   }

2015

1. CGF

   Object Completion Using k-Sparse Optimization

   P. Mavridis, I. Sipiran, A. Andreadis, and G. Papaioannou

   Computer Graphics Forum, , Oct 2015

   Abs DOI Bib PUBLICATION

   We present a new method for the completion of partial globally-symmetric 3D objects, based on the detection of partial and approximate symmetries in the object.

   @article{Mavridis2015kSparse,
     title = {Object Completion Using k-Sparse Optimization},
     author = {Mavridis, P. and Sipiran, I. and Andreadis, A. and Papaioannou, G.},
     journal = {Computer Graphics Forum},
     location = {},
     volume = {34},
     issue = {7},
     pages = {13--21},
     numpages = {},
     year = {2015},
     month = oct,
     publisher = {Wiley},
     doi = {10.1111/cgf.12741},
   }

2. SIGGRAPH

   Fractured 3D Object Restoration and Completion

   A. Andreadis, R. Gregor, I. Sipiran, P. Mavridis, G. Papaioannou, and 1 more author

   In ACM SIGGRAPH 2015 Posters, Los Angeles, CA, USA, Aug 2015

   Abs DOI Bib PUBLICATION

   We introduce a pipeline for restoration of 3D shapes based on reassembly of fragments and completion of missing parts. First results show the effectiveness of the approach for restoration of real-world fragment data from the Cultural Heritage domain.

   @inproceedings{Andreadis2015Fractured,
     title = {Fractured 3D Object Restoration and Completion},
     author = {Andreadis, A. and Gregor, R. and Sipiran, I. and Mavridis, P. and Papaioannou, G. and Schreck, T.},
     booktitle = {ACM SIGGRAPH 2015 Posters},
     location = {Los Angeles, CA, USA},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2015},
     month = aug,
     publisher = {ACM},
     doi = {10.1145/2787626.2792633},
   }

3. CBMI

   Empirical Evaluation of Dissimilarity Measures for 3D Object Retrieval with Application to Multi-Feature Retrieval

   R. Gregor, A. Lamprecht, I. Sipiran, T. Schreck, and B. Bustos

   In Proceedings of the 13th International Workshop on Content-Based Multimedia Indexing (CBMI), Prague, Czech Republic, Jun 2015

   Abs DOI Bib PUBLICATION

   We provide an extensive comparison of dissimilarity measures for 3D object retrieval. Our study is based on an encompassing set of feature extractors, dissimilarity measures, and benchmark data sets. We identify the best performing dissimilarity measures and in turn identify dependencies between well-performing dissimilarity measures and types of 3D features.

   @inproceedings{Gregor2015Empirical,
     title = {Empirical Evaluation of Dissimilarity Measures for 3D Object Retrieval with Application to Multi-Feature Retrieval},
     author = {Gregor, R. and Lamprecht, A. and Sipiran, I. and Schreck, T. and Bustos, B.},
     booktitle = {Proceedings of the 13th International Workshop on Content-Based Multimedia Indexing (CBMI)},
     location = {Prague, Czech Republic},
     volume = {},
     issue = {},
     pages = {1--6},
     numpages = {},
     year = {2015},
     month = jun,
     publisher = {IEEE},
     doi = {10.1109/CBMI.2015.7153629},
   }

4. 3DOR

   SHREC’15: Range Scans Based 3D Shape Retrieval

   A. Godil, H. Dutagaci, B. Bustos, S. Choi, S. Dong, and 12 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), Zurich, Switzerland, May 2015

   Abs DOI Bib PUBLICATION

   The objective of the SHREC’15 Range Scans based 3D Shape Retrieval track is to evaluate algorithms that match range scans of real objects to complete 3D mesh models in a target dataset.

   @inproceedings{Godil2015SHREC,
     title = {SHREC'15: Range Scans Based 3D Shape Retrieval},
     author = {Godil, A. and Dutagaci, H. and Bustos, B. and Choi, S. and Dong, S. and Furuya, T. and Li, H. and Link, N. and Moriyama, A. and Meruane, R. and Ohbuchi, R. and Paulus, D. and Schreck, T. and Seib, V. and Sipiran, I. and Yin, H. and Zhang, C.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {Zurich, Switzerland},
     volume = {},
     issue = {},
     pages = {153--160},
     numpages = {},
     year = {2015},
     month = may,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20151066},
   }

5. 3DOR

   SHREC’15: Scalability of Non-Rigid 3D Shape Retrieval

   I. Sipiran, B. Bustos, T. Schreck, A. Bronstein, S. Choi, and 4 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), Zurich, Switzerland, May 2015

   Abs DOI Bib PUBLICATION

   In the 2015 SHREC track on Scalability of 3D Shape Retrieval, we provide a benchmark with more than 96 thousand shapes. The dataset is based on a non-rigid retrieval benchmark enhanced by other existing shape benchmarks.

   @inproceedings{Sipiran2015SHREC,
     title = {SHREC'15: Scalability of Non-Rigid 3D Shape Retrieval},
     author = {Sipiran, I. and Bustos, B. and Schreck, T. and Bronstein, A. and Choi, S. and Lai, L. and Li, H. and Litman, R. and Sun, L.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {Zurich, Switzerland},
     volume = {},
     issue = {},
     pages = {121--128},
     numpages = {},
     year = {2015},
     month = may,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20151065},
   }

6. 3DOR

   Automatic 3D Object Fracturing for Evaluation of Partial Retrieval and Object Restoration Tasks - Benchmark and Application to 3D Cultural Heritage Data

   R. Gregor, D. Bauer, I. Sipiran, P. Perakis, and T. Schreck

   In Eurographics Workshop on 3D Object Retrieval (3DOR), Zurich, Switzerland, May 2015

   Abs DOI Bib PUBLICATION

   We present a methodology to automatically generate 3D fragment data based on synthetic fracturing of 3D input objects. This allows generating large-scale benchmark datasets for evaluation of partial retrieval and object restoration tasks.

   @inproceedings{Gregor2015Automatic,
     title = {Automatic 3D Object Fracturing for Evaluation of Partial Retrieval and Object Restoration Tasks - Benchmark and Application to 3D Cultural Heritage Data},
     author = {Gregor, R. and Bauer, D. and Sipiran, I. and Perakis, P. and Schreck, T.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {Zurich, Switzerland},
     volume = {},
     issue = {},
     pages = {7--14},
     numpages = {},
     year = {2015},
     month = may,
     publisher = {Eurographics Association},
     doi = {10.2312/3dor.20151049},
   }

2014

1. BDVC

   Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix

   M. Behrisch, L. Shao, B. C. Kwon, T. Schreck, I. Sipiran, and 1 more author

   In GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, Stuttgart, Germany, Sep 2014

   Abs DOI Bib PUBLICATION

   In this poster, we investigate a quality metrics-driven approach to achieve our goal for scatterplot matrices (SPLOMs). We rearrange SPLOMs by sorting scatterplots based on their locally significant visual motifs.

   @inproceedings{Behrisch2014Quality,
     title = {Quality Metrics Driven Approach to Visualize Multidimensional Data in Scatterplot Matrix},
     author = {Behrisch, M. and Shao, L. and Kwon, B. C. and Schreck, T. and Sipiran, I. and Keim, D. A.},
     booktitle = {GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing},
     location = {Stuttgart, Germany},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2014},
     month = sep,
     publisher = {Gesellschaft für Informatik},
     doi = {},
   }

2. BDVC

   Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

   L. Shao, M. Behrisch, T. Schreck, I. Sipiran, B. C. Kwon, and 1 more author

   In GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, Stuttgart, Germany, Sep 2014

   Abs DOI Bib PUBLICATION

   We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.

   @inproceedings{Shao2014Identifying,
     title = {Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices},
     author = {Shao, L. and Behrisch, M. and Schreck, T. and Sipiran, I. and Kwon, B. C. and Keim, D. A.},
     booktitle = {GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing},
     location = {Stuttgart, Germany},
     volume = {},
     issue = {},
     pages = {},
     numpages = {},
     year = {2014},
     month = sep,
     publisher = {Gesellschaft für Informatik},
     doi = {},
   }

2013

1. VisComput

   Key-components: Detection of salient regions on 3D meshes

   Ivan Sipiran and Benjamin Bustos

   The Visual Computer, , Dec 2013

   Abs DOI Bib PUBLICATION

   In this paper, we present a method to detect stable components on 3D meshes. A component is a salient region on the mesh which contains discriminative local features. Our goal is to represent a 3D mesh with a set of regions, which we called key-components, that characterize the represented object and therefore, they could be used for effective matching and recognition. As key-components are features in coarse scales, they are less sensitive to mesh deformations such as noise.

   @article{sipiran2013keycomponents,
     title = {Key-components: Detection of salient regions on 3D meshes},
     author = {Sipiran, Ivan and Bustos, Benjamin},
     journal = {The Visual Computer},
     location = {},
     volume = {29},
     issue = {12},
     pages = {1319--1332},
     numpages = {14},
     year = {2013},
     month = dec,
     publisher = {Springer},
     doi = {10.1007/s00371-013-0870-9},
   }

2. ICCV

   A Fully Hierarchical Approach for Finding Correspondences in Non-rigid Shapes

   Ivan Sipiran and Benjamin Bustos

   In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, Dec 2013

   Abs DOI Bib PUBLICATION

   This paper presents a hierarchical method for finding correspondences in non-rigid shapes. We propose a new representation for 3D meshes: the decomposition tree. This structure characterizes the recursive decomposition process of a mesh into regions of interest and keypoints. We also propose a hierarchical matching algorithm that performs in a level-wise manner. The matching process is guided by the similarity between regions in high levels of the tree, until reaching the keypoints stored in the leaves. This allows us to reduce the search space of correspondences, making also the matching process efficient.

   @inproceedings{sipiran2013hierarchical,
     title = {A Fully Hierarchical Approach for Finding Correspondences in Non-rigid Shapes},
     author = {Sipiran, Ivan and Bustos, Benjamin},
     booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
     location = {Sydney, Australia},
     pages = {817--824},
     numpages = {8},
     year = {2013},
     month = dec,
     publisher = {IEEE},
     doi = {},
   }

3. ComputGraph

   Data-aware 3D partitioning for generic shape retrieval

   Ivan Sipiran, Benjamin Bustos, and Tobias Schreck

   Computers & Graphics, , Aug 2013

   Abs DOI Bib PUBLICATION

   In this paper, we present a new approach for generic 3D shape retrieval based on a mesh partitioning scheme. Our method combines a mesh global description and mesh partition descriptions to represent a 3D shape. The partitioning is useful because it helps us to extract additional information in a more local sense. Thus, part descriptions can mitigate the semantic gap imposed by global description methods. We propose to find spatial agglomerations of local features to generate mesh partitions. Hence, the definition of a distance function is stated as an optimization problem to find the best match between two shape representations. We show that mesh partitions are representative and therefore it helps to improve the effectiveness in retrieval tasks. We present exhaustive experimentation using the SHREC’09 Generic Shape Retrieval Benchmark.

   @article{sipiran2013dataaware,
     title = {Data-aware 3D partitioning for generic shape retrieval},
     author = {Sipiran, Ivan and Bustos, Benjamin and Schreck, Tobias},
     journal = {Computers & Graphics},
     location = {},
     volume = {37},
     issue = {5},
     pages = {460--472},
     numpages = {13},
     year = {2013},
     month = aug,
     publisher = {Elsevier},
     doi = {10.1016/j.cag.2013.04.002},
   }

4. PatternRecogn

   A comparison of methods for non-rigid 3D shape retrieval

   Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, and 14 more authors

   Pattern Recognition, , Jan 2013

   Abs DOI Bib PUBLICATION

   Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures.

   @article{lian2013comparison,
     title = {A comparison of methods for non-rigid 3D shape retrieval},
     author = {Lian, Zhouhui and Godil, Afzal and Bustos, Benjamin and Daoudi, Mohamed and Hermans, Jeroen and Kawamura, Shun and Kurita, Yukinori and Lavoué, Guillaume and Nguyen, Hien Van and Ohbuchi, Ryutarou and Ohkita, Yuki and Ohishi, Yuki and Porikli, Fatih and Reuter, Martin and Sipiran, Ivan and Smeets, Dirk and Suetens, Paul and Tabia, Hedi and Vandermeulen, Dirk},
     journal = {Pattern Recognition},
     location = {},
     volume = {46},
     issue = {1},
     pages = {449--461},
     numpages = {13},
     year = {2013},
     month = jan,
     publisher = {Elsevier},
     doi = {10.1016/j.patcog.2012.07.014},
   }

5. 3DOR

   SHREC’13 Track: Large-Scale Partial Shape Retrieval Using Simulated Range Images

   Ivan Sipiran, Rodrigo Meruane, Benjamin Bustos, Tobias Schreck, H. Johan, and 2 more authors

   In Eurographics Workshop on 3D Object Retrieval (3DOR), , May 2013

   Abs DOI Bib PUBLICATION

   Partial shape retrieval is a challenging problem in content-based 3D model retrieval. This track intends to evaluate the performance of existing algorithms for partial retrieval. The contest is based on a new large-scale query set obtained by mimicking the range image acquisition using a standard 3D benchmark as target set. The query set contains 7200 partial meshes with different levels of complexity. Furthermore, we propose the use of new performance measures based on a partiality factor. With these characteristics, our goal is to evaluate several important aspects: effectiveness, efficiency, robustness and scalability. The obtained results of this track open new questions regarding the difficulty of the partial shape retrieval problem and the scalability of algorithms. In addition, potential future directions on this topic are identified.

   @inproceedings{sipiran2013shrec,
     title = {SHREC'13 Track: Large-Scale Partial Shape Retrieval Using Simulated Range Images},
     author = {Sipiran, Ivan and Meruane, Rodrigo and Bustos, Benjamin and Schreck, Tobias and Johan, H. and Li, B. and Lu, Y.},
     booktitle = {Eurographics Workshop on 3D Object Retrieval (3DOR)},
     location = {},
     pages = {81--88},
     numpages = {8},
     year = {2013},
     month = may,
     publisher = {Eurographics Association},
     doi = {},
   }

2012

1. 3DOR

   Key-component detection on 3D meshes using local features

   Ivan Sipiran and Benjamin Bustos

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’2012), Cagliari, Italy, May 2012

   Abs DOI Bib PUBLICATION

   In this paper, we present a method to detect stable components on 3D meshes. A component is a region on the mesh which contains discriminative local features. Our goal is to represent a 3D mesh with a set of regions, which we called key-components, that characterize the represented object and therefore, they could be used for effective matching and recognition. As key-components are features in coarse scales, they are less sensitive to mesh deformations such as noise.

   @inproceedings{Sipiran2012KeyComponent,
     title = {Key-component detection on 3D meshes using local features},
     author = {Sipiran, Ivan and Bustos, Benjamin},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'2012)},
     location = {Cagliari, Italy},
     pages = {25--32},
     year = {2012},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR12/025-032},
   }

2. 3DOR

   SHREC’12 Track: Stability on Abstract Shapes

   Silvia Biasotti, Xiang Bai, Benjamin Bustos, Andrea Cerri, Daniela Giorgi, and 5 more authors

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’2012), Cagliari, Italy, May 2012

   Abs DOI Bib PUBLICATION

   This contribution reports the results of the SHREC 2012 track: Stability on Abstract Shapes. This track saw six registrations of which only three participants effectively sent the results of their runs.

   @inproceedings{Biasotti2012SHREC12,
     title = {SHREC'12 Track: Stability on Abstract Shapes},
     author = {Biasotti, Silvia and Bai, Xiang and Bustos, Benjamin and Cerri, Andrea and Giorgi, Daniela and Li, Lihua and Mortara, Michela and Sipiran, Ivan and Zhang, Shuqing and Spagnuolo, Michela},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'2012)},
     location = {Cagliari, Italy},
     pages = {33--40},
     year = {2012},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR12/033-040},
   }

3. 3DImaging

   3D Shape Matching for Retrieval and Recognition

   Benjamin Bustos and Ivan Sipiran

   In 3D Imaging, Analysis and Applications, May 2012

   Abs DOI Bib PUBLICATION

   This chapter gives an overview of 3D shape matching and its applications to 3D shape retrieval and 3D shape recognition. In order to present the subject, we describe several techniques for shape matching and discuss their advantages and limitations.

   @incollection{Bustos2012ShapeMatching,
     title = {3D Shape Matching for Retrieval and Recognition},
     author = {Bustos, Benjamin and Sipiran, Ivan},
     booktitle = {3D Imaging, Analysis and Applications},
     editor = {Reinhard, Erik and Dam, Erik and Gooch, Bruce and Shirley, Peter},
     pages = {265--308},
     year = {2012},
     publisher = {Springer London},
     doi = {10.1007/978-1-4471-4063-4_7},
   }

2011

1. VisComput

   Harris 3D: A robust extension of the Harris operator for interest point detection on 3D meshes

   Ivan Sipiran and Benjamin Bustos

   The Visual Computer, May 2011

   Abs DOI Bib PUBLICATION

   In this paper, we present an interest points detector for 3D objects based on Harris operator, which has been used with good results in computer vision applications. We propose an adaptive technique to determine the neighborhood of a vertex, over which the Harris response on that vertex is calculated. Our method is robust to several transformations, which can be seen in the high repeatability values obtained using the SHREC feature detection and description benchmark.

   @article{Sipiran2011Harris3D,
     title = {Harris 3D: A robust extension of the Harris operator for interest point detection on 3D meshes},
     author = {Sipiran, Ivan and Bustos, Benjamin},
     journal = {The Visual Computer},
     volume = {27},
     number = {11},
     pages = {963--976},
     year = {2011},
     publisher = {Springer},
     doi = {10.1007/s00371-011-0610-y},
   }

2. 3DOR

   SHREC 2011: Robust Feature Detection and Description Benchmark

   Edmond Boyer, Alexander M. Bronstein, Michael M. Bronstein, Benjamin Bustos, Tamar Darom, and 13 more authors

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’2011), Llandudno, UK, May 2011

   Abs DOI Bib PUBLICATION

   Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC’11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations.

   @inproceedings{Boyer2011SHREC11,
     title = {SHREC 2011: Robust Feature Detection and Description Benchmark},
     author = {Boyer, Edmond and Bronstein, Alexander M. and Bronstein, Michael M. and Bustos, Benjamin and Darom, Tamar and Horaud, Radu and Hotz, Ingrid and Keller, Yaron and Keustermans, Jeroen and Kovnatsky, Artiom and Litman, Ron and Reininghaus, Jan and Sipiran, Ivan and Smeets, Dirk and Suetens, Paul and Vandermeulen, Dirk and Zaharescu, Andrei and Zobel, Volker},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'2011)},
     location = {Llandudno, UK},
     pages = {71--78},
     year = {2011},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR11/071-078},
   }

3. 3DOR

   SHREC’11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes

   Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, and 13 more authors

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’2011), Llandudno, UK, May 2011

   Abs DOI Bib PUBLICATION

   The aim of this track is to measure and compare the performance of non-rigid 3D shape retrieval methods implemented by different participants around the world. The track is based on a new non-rigid 3D shape benchmark, which contains 600 watertight triangle meshes that are equally classified into 30 categories.

   @inproceedings{Lian2011SHREC11NonRigid,
     title = {SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes},
     author = {Lian, Zhouhui and Godil, Afzal and Bustos, Benjamin and Daoudi, Mohamed and Hermans, Jeroen and Kawamura, Shun and Kurita, Yuki and Lavoué, Guillaume and Nguyen, Hong-Anh and Ohbuchi, Ryutarou and Ohkita, Yuki and Ohishi, Yusuke and Porikli, Fatih and Reuter, Martin and Sipiran, Ivan and Smeets, Dirk and Suetens, Paul and Vandermeulen, Dirk},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'2011)},
     location = {Llandudno, UK},
     pages = {79--88},
     year = {2011},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR11/079-088},
   }

2010

1. 3DOR

   A Robust 3D Interest Points Detector Based on Harris Operator

   Ivan Sipiran and Benjamin Bustos

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’2010), Norrköping, Sweden, May 2010

   Abs DOI Bib PUBLICATION

   We propose an adaptive technique to determine the neighborhood of a vertex, over which the Harris response on that vertex is calculated. Our method is robust to several transformations, which can be seen in the high repeatability values obtained using the SHREC feature detection and description benchmark.

   @inproceedings{Sipiran2010Harris3D,
     title = {A Robust 3D Interest Points Detector Based on Harris Operator},
     author = {Sipiran, Ivan and Bustos, Benjamin},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'2010)},
     location = {Norrköping, Sweden},
     pages = {7--14},
     year = {2010},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR10/007-014},
   }

2. 3DOR

   SHREC 2010: robust feature detection and description benchmark

   Alexander M. Bronstein, Michael M. Bronstein, Benjamin Bustos, Umberto Castellani, Marco Crisani, and 9 more authors

   In Proc. Eurographics Workshop on 3D Object Retrieval (3DOR’10), Norrköping, Sweden, May 2010

   Abs DOI Bib PUBLICATION

   Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC’10 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC’10 robust feature detection and description benchmark results.

   @inproceedings{Bronstein2010SHREC,
     title = {SHREC 2010: robust feature detection and description benchmark},
     author = {Bronstein, Alexander M. and Bronstein, Michael M. and Bustos, Benjamin and Castellani, Umberto and Crisani, Marco and Falcidieno, Bianca and Guibas, Leonidas and Kokkinos, Iasonas and Murino, Vittorio and Sipiran, Ivan and Ovsjanikov, Maks and Patane, Giuseppe and Spagnuolo, Michela and Sun, Jian},
     booktitle = {Proc. Eurographics Workshop on 3D Object Retrieval (3DOR'10)},
     location = {Norrköping, Sweden},
     pages = {79--86},
     year = {2010},
     publisher = {Eurographics Association},
     doi = {10.2312/3DOR/3DOR10/079-086},
   }