Recent advances in shape correspondence

Sahillioğlu, Yusuf

doi:10.1007/s00371-019-01760-0

Cited by 102 publications

(60 citation statements)

References 100 publications

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“…Below we review the work most closely related to ours and focus primarily on the functional maps framework. We refer the interested readers to recent surveys including [43,42,3,38] for an in-depth treatment of other shape matching approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Among many existing approaches for this problem [43,38], a prominent overall strategy is to exploit spectral quantities, such as the eigenfunctions of the Laplace-Beltrami operator, which are naturally invariant to isometric shape deformations. Within this category, the functional map framework, introduced in [28] proposes an efficient way to represent and compute mappings and achieves the state-ofthe-art accuracy in difficult shape matching problems [23].…”

Section: Introductionmentioning

confidence: 99%

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Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

Pai

Ren

Melzi

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

Pai

Ren

Melzi

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…3D shape registration 3D shape registration is a problem that has been extensively covered. We refer the reader to surveys such as [38,33,7] for a general overview, and only mention here methods related to our work.…”

Section: Shape Correspondence and Registrationmentioning

confidence: 99%

Multi-scale Space-time Registration of Growing Plants

Pan

Hétroy-Wheeler

Charlaix

et al. 2021

2021 International Conference on 3D Vision (3DV)

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In this paper, we introduce a new method for the spacetime registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP.We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.

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“…41a nd similarly Reference 42 first embeds the shape into isometric representation. Then, they optimize this embedding as a variant of the classical intrinsic distortion [43][44][45] objective by using Markov Random Field optimization. Nonisometric version of this study achieves reconstruction of skulls by using volumetric Dirichlet energy.…”

Section: Related Workmentioning

confidence: 99%

Human body reconstruction from limited number of points

Taştan

Sahillioğlu

2021

Computer Animation & Virtual

Self Cite

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We propose a novel approach for reconstructing plausible three-dimensional (3D) human body models from small number of 3D points which represent body parts. We leverage a database of 3D models of humans varying from each other by physical attributes such as age, gender, weight, and height. First we divide the bodies in database into seven semantic regions. Then, for each input region consisting of maximum 40 points, we search the database for the best matching body part. For the matching criterion, we use the distance between novel point-based features of input points and body parts in the database. We then combine the matched parts from different bodies into one body, with the help of Laplacian deformation, which results in a plausible human body. To evaluate our results objectively, we pick points from each part of the ground-truth human body models, then reconstruct them using our method and compare the resulting bodies with the corresponding ground-truths. Also, our results are compared with registration-based results. In addition, we run our algorithm with noisy data to test the robustness of our method and run it with input points whose body parts are manually edited, which produces plausible human bodies that do not even exist in our database. Our experiments verify qualitatively and quantitatively that the proposed approach reconstructs human bodies with different physical attributes from a small number of points using a small database.

show abstract

Recent advances in shape correspondence

Cited by 102 publications

References 100 publications

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

Multi-scale Space-time Registration of Growing Plants

Human body reconstruction from limited number of points

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