Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matching
- URL: http://arxiv.org/abs/2407.08244v1
- Date: Thu, 11 Jul 2024 07:45:06 GMT
- Title: Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matching
- Authors: Dongliang Cao, Zorah Laehner, Florian Bernard,
- Abstract summary: We propose a synchronous diffusion process which we use as regularisation to achieve smoothness in non-rigid 3D shape matching problems.
We demonstrate that our novel regularisation can substantially improve the state-of-the-art in shape matching, especially in the presence of topological noise.
- Score: 15.843208029973175
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Most recent unsupervised non-rigid 3D shape matching methods are based on the functional map framework due to its efficiency and superior performance. Nevertheless, respective methods struggle to obtain spatially smooth pointwise correspondences due to the lack of proper regularisation. In this work, inspired by the success of message passing on graphs, we propose a synchronous diffusion process which we use as regularisation to achieve smoothness in non-rigid 3D shape matching problems. The intuition of synchronous diffusion is that diffusing the same input function on two different shapes results in consistent outputs. Using different challenging datasets, we demonstrate that our novel regularisation can substantially improve the state-of-the-art in shape matching, especially in the presence of topological noise.
Related papers
- Diff-Reg v1: Diffusion Matching Model for Registration Problem [34.57825794576445]
Existing methods commonly leverage geometric or semantic point features to generate potential correspondences.
Previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios.
We introduce a diffusion matching model for robust correspondence estimation.
arXiv Detail & Related papers (2024-03-29T02:10:38Z) - Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation [50.376243444909136]
We present a unified framework to predict both point-wise correspondences and shape between 3D shapes.
We combine the deep functional map framework with classical surface deformation models to map shapes in both spectral and spatial domains.
arXiv Detail & Related papers (2024-02-29T07:26:23Z) - Deformation-Guided Unsupervised Non-Rigid Shape Matching [7.327850781641328]
We present an unsupervised data-driven approach for non-rigid shape matching.
Our approach is particularly robust when matching digitized shapes using 3D scanners.
arXiv Detail & Related papers (2023-11-27T09:55:55Z) - Neural Semantic Surface Maps [52.61017226479506]
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another.
Our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement.
arXiv Detail & Related papers (2023-09-09T16:21:56Z) - Multiway Non-rigid Point Cloud Registration via Learned Functional Map
Synchronization [105.14877281665011]
We present SyNoRiM, a novel way to register multiple non-rigid shapes by synchronizing the maps relating learned functions defined on the point clouds.
We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in registration accuracy.
arXiv Detail & Related papers (2021-11-25T02:37:59Z) - Learning Iterative Robust Transformation Synchronization [71.73273007900717]
We propose to use graph neural networks (GNNs) to learn transformation synchronization.
In this work, we avoid handcrafting robust loss functions, and propose to use graph neural networks (GNNs) to learn transformation synchronization.
arXiv Detail & Related papers (2021-11-01T07:03:14Z) - Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes [86.2129580231191]
Adjoint Rigid Transform (ART) Network is a neural module which can be integrated with a variety of 3D networks.
ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks.
We will release our code and pre-trained models for further research.
arXiv Detail & Related papers (2021-02-01T20:58:45Z) - Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames.
We show that our approach significantly improves accuracy, scalability, and robustness against noise.
arXiv Detail & Related papers (2020-06-15T09:15:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.