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Related papers: Non-Rigid 3D Shape Correspondences: From Foundatio…

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We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a deep neural network. This neural deformation graph…

Computer Vision and Pattern Recognition · Computer Science 2020-12-04 Aljaž Božič , Pablo Palafox , Michael Zollhöfer , Justus Thies , Angela Dai , Matthias Nießner

Using (casual) images to texture 3D models is a common way to create realistic 3D models, which is a very important task in computer graphics. However, if the shape of the casual image does not look like the target model or the target…

Graphics · Computer Science 2018-09-11 I-Chao Shen , Yi-Hau Wang , Yu-Mei Chen , Bing-Yu Chen

In recent years, the usefulness of 3D shape estimation is being realized in microscopic or close-range imaging, as the 3D information can further be used in various applications. Due to limited depth of field at such small distances, the…

Computer Vision and Pattern Recognition · Computer Science 2017-01-02 Arnav Bhavsar

We propose a deep learning approach for finding dense correspondences between 3D scans of people. Our method requires only partial geometric information in the form of two depth maps or partial reconstructed surfaces, works for humans in…

Computer Vision and Pattern Recognition · Computer Science 2016-06-28 Lingyu Wei , Qixing Huang , Duygu Ceylan , Etienne Vouga , Hao Li

Reconstruction of a 3D shape from a single 2D image is a classical computer vision problem, whose difficulty stems from the inherent ambiguity of recovering occluded or only partially observed surfaces. Recent methods address this challenge…

Computer Vision and Pattern Recognition · Computer Science 2020-02-04 Yuan Yao , Nico Schertler , Enrique Rosales , Helge Rhodin , Leonid Sigal , Alla Sheffer

The advent of high resolution imaging has made data on surface shape widespread. Methods for the analysis of shape based on landmarks are well established but high resolution data require a functional approach. The starting point is a…

Computer Vision and Pattern Recognition · Computer Science 2020-03-20 Stanislav Katina , Liberty Vittert , Adrian W. Bowman

The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly…

Computer Vision and Pattern Recognition · Computer Science 2018-04-05 Or Litany , Alex Bronstein , Michael Bronstein , Ameesh Makadia

We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface representations are more effective at modeling high-resolution…

Computer Vision and Pattern Recognition · Computer Science 2022-08-19 Gopal Sharma , Kangxue Yin , Subhransu Maji , Evangelos Kalogerakis , Or Litany , Sanja Fidler

An important operation in geometry processing is finding the correspondences between pairs of shapes. The Gromov-Hausdorff distance, a measure of dissimilarity between metric spaces, has been found to be highly useful for nonrigid shape…

Computer Vision and Pattern Recognition · Computer Science 2013-11-25 Alon Shtern , Ron Kimmel

Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when presented with challenges innate to real-world data such as…

Computer Vision and Pattern Recognition · Computer Science 2022-07-22 Ramana Sundararaman , Gautam Pai , Maks Ovsjanikov

Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D…

Computer Vision and Pattern Recognition · Computer Science 2021-03-01 Araceli Morales , Gemma Piella , Federico M. Sukno

State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions. Meanwhile, recent advances in the functional map framework…

Computer Vision and Pattern Recognition · Computer Science 2022-04-29 Nicolas Donati , Etienne Corman , Maks Ovsjanikov

In this paper, we propose a fully differentiable pipeline for estimating accurate dense correspondences between 3D point clouds. The proposed pipeline is an extension and a generalization of the functional maps framework. However, instead…

Computer Vision and Pattern Recognition · Computer Science 2020-10-27 Riccardo Marin , Marie-Julie Rakotosaona , Simone Melzi , Maks Ovsjanikov

For the shape control of deformable free-form surfaces, simulation plays a crucial role in establishing the mapping between the actuation parameters and the deformed shapes. The differentiation of this forward kinematic mapping is usually…

Robotics · Computer Science 2024-05-16 Yingjun Tian , Guoxin Fang , Renbo Su , Weiming Wang , Simeon Gill , Andrew Weightman , Charlie C. L. Wang

Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape.…

Graphics · Computer Science 2023-03-13 Robin Magnet , Maks Ovsjanikov

This paper addresses how to construct features for the problem of image correspondence, in particular, the paper addresses how to construct features so as to maintain the right level of invariance versus discriminability. We show that…

Computer Vision and Pattern Recognition · Computer Science 2012-11-21 Ganesh Sundaramoorthi , Yanchao Yang

Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust contacts with these fixtures, accurate estimation of the…

Robotics · Computer Science 2024-01-31 Kejia Chen , Zhenshan Bing , Yansong Wu , Fan Wu , Liding Zhang , Sami Haddadin , Alois Knoll

3D local feature extraction and matching is the basis for solving many tasks in the area of computer vision, such as 3D registration, modeling, recognition and retrieval. However, this process commonly draws into false correspondences, due…

Computer Vision and Pattern Recognition · Computer Science 2019-01-17 Bao Zhao , Xiaobo Chen , Xinyi Le , Juntong Xi

We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer can accurately edit the source mesh following the shape…

Computer Vision and Pattern Recognition · Computer Science 2023-07-20 Hao Su , Xuefeng Liu , Jianwei Niu , Ji Wan , Xinghao Wu

Correspondences estimation or feature matching is a key step in the image-based 3D reconstruction problem. In this paper, we propose two algebraic properties for correspondences. The first is a rank deficient matrix construct from the…

Computational Geometry · Computer Science 2022-05-04 Trung-Kien Le , Ping Li
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