Shape Vision Lab

Av. Beauchef 851

8370458 Santiago, Chile

West Building 314, 304

Welcome to the Shape Vision Lab at the University of Chile.

We are a research group dedicated to exploring the geometric and visual properties of 3D data. Our work lies at the intersection of shape analysis, 3D computer vision, and geometry processing. We investigate both theoretical foundations and practical applications, from symmetry-aware learning to generative models for 3D shapes.

Our team includes undergraduate and graduate students, as well as collaborators from academia and industry. Together, we tackle problems in object recognition, generative modeling, cultural heritage reconstruction, and more.

Feel free to explore our ongoing projects, publications, and student theses. We are always open to new collaborations and ideas—whether you’re a student, researcher, or industry partner interested in the 3D world.

News

Sep 01, 2025	Joaquín Cruz's MSc Thesis Defense
Aug 29, 2025	We were present at the III Graduate Symposium, organized by the Faculty of Physical and Mathematical Sciences at the University of Chile
Aug 07, 2025	Vicente Muñoz's Thesis Presentation
Aug 04, 2025	Nicolas Caytuiro's Doctoral Qualifying Exam
Jun 25, 2025	Louise Schmidt's Thesis Presentation

Selected Publications

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 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 = {}, }

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 Bib PUBLICATION

@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},
}

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 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}, }

CACM

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

I. Sipiran

Communications of the ACM, New York, Aug 2024

Abs Bib PUBLICATION

@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},
}

VisComp

A Convolutional Architecture for 3D Model Embedding Using Image Views

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

The Visual Computer, , 2024

Abs Bib PUBLICATION

@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},
}

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 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}, }

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 Bib PUBLICATION

@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 = {},
}

VISART
Cultural Heritage 3D Reconstruction with Diffusion Networks

P. Jaramillo and I. Sipiran

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

Abs 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 = {}, }

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 Bib PUBLICATION

@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 = {},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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 = {},
}

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 Bib PUBLICATION

@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 = {},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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 = {},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

AutCon

Automatic Floor Plan Analysis and Recognition

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

Automation in Construction, , Aug 2022

Abs Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

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 Bib PUBLICATION

@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},
}

3DOR

Completion of Cultural Heritage Objects with Rotational Symmetry

Iván Sipiran

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

Abs Bib PUBLICATION

@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},
}

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 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}, }

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 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},
}

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 Bib PUBLICATION

@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},
}

CGF

Object Completion Using k-Sparse Optimization

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

Computer Graphics Forum, , Oct 2015

Abs Bib PUBLICATION

@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},
}

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 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}, }

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 Bib PUBLICATION

@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},
}

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

Ivan Sipiran and Benjamin Bustos

The Visual Computer, , Dec 2013

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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}, }
ComputGraph
Data-aware 3D partitioning for generic shape retrieval

Ivan Sipiran, Benjamin Bustos, and Tobias Schreck

Computers & Graphics, , Aug 2013

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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}, }
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

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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 = {}, }
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 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}, }

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 Bib PUBLICATION

@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},
}

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

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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}, }
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 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}, }
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 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}, }