3DOR 2020 - Short Papers

Permanent URI for this collection

Graz, Austria | September 4 -- 5, 2020 | Held as Virtual Open Workshop

3DOR Short Papers
Shape Correspondence by Aligning Scale-invariant LBO Eigenfunctions
Amit Bracha, Oshri Halimi, and Ron Kimmel
Fast Feature Curve Extraction for Similarity Estimation of 3D Meshes
Ioannis Romanelis, Gerasimos Arvanitis, and Konstantinos Moustakas
SHREC Short Papers
SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations
Roberto M. Dyke, Feng Zhou, Yu-Kun Lai, Paul L. Rosin, Daoliang Guo, Kun Li, Riccardo Marin, and Jingyu Yang
SHREC 2020 Track: River Gravel Characterization
Andrea Giachetti, Silvia Biasotti, Elia Moscoso Thompson, Luigi Fraccarollo, Quang Nguyen, Hai-Dang Nguyen, Minh-Triet Tran, Gerasimos Arvanitis, Ioannis Romanelis, Vlasis Fotis, Konstantinos Moustakas, Claudio Tortorici, Naoufel Werghi, and Stefano Berretti
SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval
Wenhui Li, Dan Song, Anan Liu, Weizhi Nie, Ting Zhang, Xiaoqian Zhao, Mingsheng Ma, Yuqian Li, Heyu Zhou, Beibei Zhang, Shengjie Le, Dandan Wang, Tongwei Ren, Gangshan Wu, The-Anh Vu-Le, Xuan-Nhat Hoang, E-Ro Nguyen, Thang-Long Nguyen-Ho, Hai-Dang Nguyen, Trong-Le Do, and Minh-Triet Tran
SHREC 2020 Track: 6D Object Pose Estimation
Honglin Yuan, Remco C. Veltkamp, Georgios Albanis, Nikolaos Zioulis, Dimitrios Zarpalas, and Petros Daras

BibTeX (3DOR 2020 - Short Papers)
@inproceedings{
10.2312:3dor.20201159,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
Shape Correspondence by Aligning Scale-invariant LBO Eigenfunctions}},
author = {
Bracha, Amit
 and
Halim, Oshri
 and
Kimmel, Ron
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201159}
}
@inproceedings{
10.2312:3dor.20201161,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations}},
author = {
Dyke, Roberto M.
 and
Zhou, Feng
 and
Lai, Yu-Kun
 and
Rosin, Paul L.
 and
Guo, Daoliang
 and
Li, Kun
 and
Marin, Riccardo
 and
Yang, Jingyu
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201161}
}
@inproceedings{
10.2312:3dor.20201160,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
Fast Feature Curve Extraction for Similarity Estimation of 3D Meshes}},
author = {
Romanelis, Ioannis
 and
Arvanitis, Gerasimos
 and
Moustakas, Konstantinos
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201160}
}
@inproceedings{
10.2312:3dor.20201162,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
SHREC 2020 Track: River Gravel Characterization}},
author = {
Giachetti, Andrea
 and
Biasotti, Silvia
 and
Moustakas, Konstantinos
 and
Tortorici, Claudio
 and
Werghi, Naoufel
 and
Berretti, Stefano
 and
Moscoso Thompson, Elia
 and
Fraccarollo, Luigi
 and
Nguyen, Quang
 and
Nguyen, Hai-Dang
 and
Tran, Minh-Triet
 and
Arvanitis, Gerasimos
 and
Romanelis, Ioannis
 and
Fotis, Vlasis
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201162}
}
@inproceedings{
10.2312:3dor.20201163,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval}},
author = {
Li, Wenhui
 and
Song, Dan
 and
Le, Shengjie
 and
Wang, Dandan
 and
Ren, Tongwei
 and
Wu, Gangshan
 and
Vu-Le, The-Anh
 and
Hoang, Xuan-Nhat
 and
Nguyen, E-Ro
 and
Nguyen-Ho, Thang-Long
 and
Nguyen, Hai-Dang
 and
Do, Trong-Le
 and
Liu, Anan
 and
Tran, Minh-Triet
 and
Nie, Weizhi
 and
Zhang, Ting
 and
Zhao, Xiaoqian
 and
Ma, Mingsheng
 and
Li, Yuqian
 and
Zhou, Heyu
 and
Zhang, Beibei
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201163}
}
@inproceedings{
10.2312:3dor.20201164,
booktitle = {
Eurographics Workshop on 3D Object Retrieval},
editor = {
Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
}, title = {{
SHREC 2020 Track: 6D Object Pose Estimation}},
author = {
Yuan, Honglin
 and
Veltkamp, Remco C.
 and
Albanis, Georgios
 and
Zioulis, Nikolaos
 and
Zarpalas, Dimitrios
 and
Daras, Petros
}, year = {
2020},
publisher = {
The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {
10.2312/3dor.20201164}
}

Browse

Recent Submissions

Now showing 1 - 7 of 7
  • Item
    Eurographics Workshop on 3D Object Retrieval: Short Papers Frontmatter
    (The Eurographics Association, 2020) Schreck, Tobias; Theoharis, Theoharis; Pratikakis, Ioannis; Spagnuolo, Michela; Veltkamp, Remco C.; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
  • Item
    Shape Correspondence by Aligning Scale-invariant LBO Eigenfunctions
    (The Eurographics Association, 2020) Bracha, Amit; Halim, Oshri; Kimmel, Ron; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    When matching non-rigid shapes, the regular or scale-invariant Laplace-Beltrami Operator (LBO) eigenfunctions could potentially serve as intrinsic descriptors which are invariant to isometric transformations. However, the computed eigenfunctions of two quasi-isometric surfaces could be substantially different. Such discrepancies include sign ambiguities and possible rotations and reflections within subspaces spanned by eigenfunctions that correspond to similar eigenvalues. Thus, without aligning the corresponding eigenspaces it is difficult to use the eigenfunctions as descriptors. Here, we propose to model the relative transformation between the eigenspaces of two quasi-isometric shapes using a band orthogonal matrix, as well as present a framework that aims to estimate this matrix. Estimating this transformation allows us to align the eigenfunctions of one shape with those of the other, that could then be used as intrinsic, consistent, and robust descriptors. To estimate the transformation we use an unsupervised spectral-net framework that uses descriptors given by the eigenfunctions of the scale-invariant version of the LBO. Then, using a spectral training mechanism, we find a band limited orthogonal matrix that aligns the two sets of eigenfunctions.
  • Item
    SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations
    (The Eurographics Association, 2020) Dyke, Roberto M.; Zhou, Feng; Lai, Yu-Kun; Rosin, Paul L.; Guo, Daoliang; Li, Kun; Marin, Riccardo; Yang, Jingyu; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Commonly, novel non-rigid shape correspondence techniques focus on particular matching challenges. This can lead to the potential trade-off of poorer performance in other scenarios. An ideal dataset would provide a granular means for degrees of evaluation. In this paper, we propose a novel dataset of real scans that contain challenging non-isometric deformations to evaluate non-rigid point-to-point correspondence and registration algorithms. The deformations included in our dataset cover extreme types of physically-based contortions of a toy rabbit. Furthermore, shape pairs contain incrementally different types and amounts of deformation, this enables performance to be systematically evaluated with respect to the nature of the deformation. A brief investigation into different methods for initialising correspondence was undertaken, and a series of experiments were subsequently conducted to investigate the performance of state-of-the-art methods on the proposed dataset. We find that methods that rely on initial correspondences and local descriptors that are sensitive to local surface changes perform poorly in comparison to other strategies, and that a template-based approach performs the best.
  • Item
    Fast Feature Curve Extraction for Similarity Estimation of 3D Meshes
    (The Eurographics Association, 2020) Romanelis, Ioannis; Arvanitis, Gerasimos; Moustakas, Konstantinos; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Feature extraction from 3D triangle meshes is a very popular and important task that could contribute to many scientific fields such as computer vision, pattern recognition, medical 3D modeling, etc. However, the main challenge is not just finding corners and edges of 3D models but to automatically extract connected clusters of vertices that jointly represent a feature curve. This paper presents an approach for feature curve extraction and similarity evaluation among feature curves of the same or other models robust to differences in scale, resolution quality, pose, or partial observation. The proposed approach could be used, as a pre-processing step, in many other applications like registration, partial matching, tracking, object recognition, etc. Extensive evaluation studies and experiments carried out using a variety of different models and use cases, verify that the proposed approach achieves accurate feature curve extraction and categorization, robust to several constraints like scale or resolution.
  • Item
    SHREC 2020 Track: River Gravel Characterization
    (The Eurographics Association, 2020) Giachetti, Andrea; Biasotti, Silvia; Moscoso Thompson, Elia; Fraccarollo, Luigi; Nguyen, Quang; Nguyen, Hai-Dang; Tran, Minh-Triet; Arvanitis, Gerasimos; Romanelis, Ioannis; Fotis, Vlasis; Moustakas, Konstantinos; Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    The quantitative analysis of the distribution of the different types of sands, gravels and cobbles shaping river beds is a very important task performed by hydrologists to derive useful information on fluvial dynamics and related processes (e.g., hydraulic resistance, sediment transport and erosion, habitat suitability. As the methods currently employed in the practice to perform this evaluation are expensive and time-consuming, the development of fast and accurate methods able to provide a reasonable estimate of the gravel distribution based on images or 3D scanning data would be extremely useful to support hydrologists in their work. To evaluate the suitability of state-of-the-art geometry processing tool to estimate the distribution from digital surface data, we created, therefore, a dataset including real captures of riverbed mockups, designed a retrieval task on it and proposed them as a challenge of the 3D Shape Retrieval Contest (SHREC) 2020. In this paper, we discuss the results obtained by the methods proposed by the groups participating in the contest and baseline methods provided by the organizers. Retrieval methods have been compared using the precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall. Results show the feasibility of gravels characterization from captured surfaces and issues in the discrimination of mixture of gravels of different size.
  • Item
    SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval
    (The Eurographics Association, 2020) Li, Wenhui; Song, Dan; Liu, Anan; Nie, Weizhi; Zhang, Ting; Zhao, Xiaoqian; Ma, Mingsheng; Li, Yuqian; Zhou, Heyu; Zhang, Beibei; Le, Shengjie; Wang, Dandan; Ren, Tongwei; Wu, Gangshan; Vu-Le, The-Anh; Hoang, Xuan-Nhat; Nguyen, E-Ro; Nguyen-Ho, Thang-Long; Nguyen, Hai-Dang; Do, Trong-Le; Tran, Minh-Triet; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Monocular image based 3D object retrieval has attracted more and more attentions in the field of 3D object retrieval. However, the research of 3D object retrieval based on 2D image is still challenging, mainly because of the gap between data from different modalities. To further support this research, we extend the previous track SHREC19'MI3DOR to organize this track, and we construct the expanded monocular image based 3D object retrieval benchmark. Compared with SHREC19'MI3DOR, this benchmark adds 19 categories for both 2D images and 3D models to the original 21 categories, taking into account the lack of categories for practical applications. Two groups participated, proposed three kinds of supervised methods and submitted 20 runs in total, and 7 commonly-used criteria are used to evaluate the retrieval performance. The results show that supervised methods still achieve satisfying retrieval results (Best NN is 96.7% for 40 categories), which are comparable to the results of SHREC19'MI3DOR. In the future, unsupervised methods are encouraged to discover in monocular image based 3D model retrieval.
  • Item
    SHREC 2020 Track: 6D Object Pose Estimation
    (The Eurographics Association, 2020) Yuan, Honglin; Veltkamp, Remco C.; Albanis, Georgios; Zioulis, Nikolaos; Zarpalas, Dimitrios; Daras, Petros; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. However, the problem is challenging due to the variety of objects in the real world. They have varying 3D shape and their appearances in captured images are affected by sensor noise, changing lighting conditions and occlusions between objects. Different pose estimation methods have different strengths and weaknesses, depending on feature representations and scene contents. At the same time, existing 3D datasets that are used for data-driven methods to estimate 6D poses have limited view angles and low resolution. To address these issues, we organize the Shape Retrieval Challenge benchmark on 6D pose estimation and create a physically accurate simulator that is able to generate photo-realistic color-and-depth image pairs with corresponding ground truth 6D poses. From captured color and depth images, we use this simulator to generate a 3D dataset which has 400 photo-realistic synthesized color-and-depth image pairs with various view angles for training, and another 100 captured and synthetic images for testing. Five research groups register in this track and two of them submitted their results. Data-driven methods are the current trend in 6D object pose estimation and our evaluation results show that approaches which fully exploit the color and geometric features are more robust for 6D pose estimation of reflective and texture-less objects and occlusion. This benchmark and comparative evaluation results have the potential to further enrich and boost the research of 6D object pose estimation and its applications.