A Survey of Non-Rigid 3D Registration
- URL: http://arxiv.org/abs/2203.07858v1
- Date: Fri, 11 Mar 2022 15:54:19 GMT
- Title: A Survey of Non-Rigid 3D Registration
- Authors: Bailin Deng and Yuxin Yao and Roberto M. Dyke and Juyong Zhang
- Abstract summary: Non-rigid registration computes an alignment between a source surface and a target surface in a non-rigid manner.
Non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications.
- Score: 28.853099966806056
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Non-rigid registration computes an alignment between a source surface with a
target surface in a non-rigid manner. In the past decade, with the advances in
3D sensing technologies that can measure time-varying surfaces, non-rigid
registration has been applied for the acquisition of deformable shapes and has
a wide range of applications. This survey presents a comprehensive review of
non-rigid registration methods for 3D shapes, focusing on techniques related to
dynamic shape acquisition and reconstruction. In particular, we review
different approaches for representing the deformation field, and the methods
for computing the desired deformation. Both optimization-based and
learning-based methods are covered. We also review benchmarks and datasets for
evaluating non-rigid registration methods, and discuss potential future
research directions.
Related papers
- 3D Representation Methods: A Survey [0.0]
3D representation has experienced significant advancements, driven by the increasing demand for high-fidelity 3D models in various applications.
This review examines the development and current state of 3D representation methods, highlighting their research trajectories, innovations, strength and weakness.
arXiv Detail & Related papers (2024-10-09T02:01:05Z) - Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks [9.388897214344572]
Three-dimensional (3D) reconstruction from two-dimensional images is an active research field in computer vision.
Traditionally, parametric techniques have been employed for this task.
Recent advancements have seen a shift towards learning-based methods.
arXiv Detail & Related papers (2024-08-29T11:16:34Z) - Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review [0.08823202672546056]
This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views.
An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies.
arXiv Detail & Related papers (2024-05-06T12:32:38Z) - 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) - NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion [56.98287481620215]
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
Our approach builds on two recent developments: surface reconstruction using neural radiance fields for the reconstruction of the visible parts of the surface, and guidance of pre-trained 2D diffusion models in the form of Score Distillation Sampling (SDS) to complete the shape in unobserved regions in a plausible manner.
arXiv Detail & Related papers (2023-12-07T19:30:55Z) - 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) - Uncertainty Guided Policy for Active Robotic 3D Reconstruction using
Neural Radiance Fields [82.21033337949757]
This paper introduces a ray-based volumetric uncertainty estimator, which computes the entropy of the weight distribution of the color samples along each ray of the object's implicit neural representation.
We show that it is possible to infer the uncertainty of the underlying 3D geometry given a novel view with the proposed estimator.
We present a next-best-view selection policy guided by the ray-based volumetric uncertainty in neural radiance fields-based representations.
arXiv Detail & Related papers (2022-09-17T21:28:57Z) - Probabilistic Registration for Gaussian Process 3D shape modelling in
the presence of extensive missing data [63.8376359764052]
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data.
Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears.
arXiv Detail & Related papers (2022-03-26T16:48:27Z) - A comprehensive survey on point cloud registration [11.69025325594053]
This survey conducts a comprehensive survey, including both same-source and cross-source registration methods.
Survey builds a new benchmark to evaluate the state-of-the-art registration algorithms in solving cross-source challenges.
arXiv Detail & Related papers (2021-03-03T21:17:06Z) - 3D Registration for Self-Occluded Objects in Context [66.41922513553367]
We introduce the first deep learning framework capable of effectively handling this scenario.
Our method consists of an instance segmentation module followed by a pose estimation one.
It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure.
arXiv Detail & Related papers (2020-11-23T08:05:28Z)
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.