Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for
Cortical Surface Reconstruction
- URL: http://arxiv.org/abs/2307.12299v1
- Date: Sun, 23 Jul 2023 11:32:14 GMT
- Title: Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for
Cortical Surface Reconstruction
- Authors: Shanlin Sun, Thanh-Tung Le, Chenyu You, Hao Tang, Kun Han, Haoyu Ma,
Deying Kong, Xiangyi Yan, Xiaohui Xie
- Abstract summary: Hybrid-CSR is a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction.
Our method unifies explicit (oriented point clouds) and implicit (indicator function) cortical surface reconstruction.
- Score: 28.31844964164312
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present Hybrid-CSR, a geometric deep-learning model that combines explicit
and implicit shape representations for cortical surface reconstruction.
Specifically, Hybrid-CSR begins with explicit deformations of template meshes
to obtain coarsely reconstructed cortical surfaces, based on which the oriented
point clouds are estimated for the subsequent differentiable poisson surface
reconstruction. By doing so, our method unifies explicit (oriented point
clouds) and implicit (indicator function) cortical surface reconstruction.
Compared to explicit representation-based methods, our hybrid approach is more
friendly to capture detailed structures, and when compared with implicit
representation-based methods, our method can be topology aware because of
end-to-end training with a mesh-based deformation module. In order to address
topology defects, we propose a new topology correction pipeline that relies on
optimization-based diffeomorphic surface registration. Experimental results on
three brain datasets show that our approach surpasses existing implicit and
explicit cortical surface reconstruction methods in numeric metrics in terms of
accuracy, regularity, and consistency.
Related papers
- InfoNorm: Mutual Information Shaping of Normals for Sparse-View Reconstruction [15.900375207144759]
3D surface reconstruction from multi-view images is essential for scene understanding and interaction.
Recent implicit surface representations, such as Neural Radiance Fields (NeRFs) and signed distance functions (SDFs) employ various geometric priors to resolve the lack of observed information.
We propose regularizing the geometric modeling by explicitly encouraging the mutual information among surface normals of highly correlated scene points.
arXiv Detail & Related papers (2024-07-17T15:46:25Z) - Reconstruction of Cortical Surfaces with Spherical Topology from Infant
Brain MRI via Recurrent Deformation Learning [16.9042503785353]
Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function.
Here, we present a method for simultaneous and spherical mapping efficiently within seconds.
We demonstrate the efficacy of our approach on infant brain MRI, which poses significant challenges to CSR.
arXiv Detail & Related papers (2023-12-10T20:20:16Z) - 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) - $p$-Poisson surface reconstruction in curl-free flow from point clouds [5.330266804358638]
Implicit neural representations (INRs) have emerged as a promising approach to surface reconstruction.
In this paper, we show that proper supervision of partial differential equations and fundamental properties of differential vector fields are sufficient to robustly reconstruct high-quality surfaces.
arXiv Detail & Related papers (2023-10-31T00:20:24Z) - NeuralMeshing: Differentiable Meshing of Implicit Neural Representations [63.18340058854517]
We propose a novel differentiable meshing algorithm for extracting surface meshes from neural implicit representations.
Our method produces meshes with regular tessellation patterns and fewer triangle faces compared to existing methods.
arXiv Detail & Related papers (2022-10-05T16:52:25Z) - Neural Template: Topology-aware Reconstruction and Disentangled
Generation of 3D Meshes [52.038346313823524]
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology.
Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
arXiv Detail & Related papers (2022-06-10T08:32:57Z) - A Level Set Theory for Neural Implicit Evolution under Explicit Flows [102.18622466770114]
Coordinate-based neural networks parameterizing implicit surfaces have emerged as efficient representations of geometry.
We present a framework that allows applying deformation operations defined for triangle meshes onto such implicit surfaces.
We show that our approach exhibits improvements for applications like surface smoothing, mean-curvature flow, inverse rendering and user-defined editing on implicit geometry.
arXiv Detail & Related papers (2022-04-14T17:59:39Z) - Shape As Points: A Differentiable Poisson Solver [118.12466580918172]
In this paper, we introduce a differentiable point-to-mesh layer using a differentiable formulation of Poisson Surface Reconstruction (PSR)
The differentiable PSR layer allows us to efficiently and differentiably bridge the explicit 3D point representation with the 3D mesh via the implicit indicator field.
Compared to neural implicit representations, our Shape-As-Points (SAP) model is more interpretable, lightweight, and accelerates inference time by one order of magnitude.
arXiv Detail & Related papers (2021-06-07T09:28:38Z) - Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [131.50372468579067]
We represent the reconstructed surface as an atlas, using a neural network.
We empirically show that our method achieves results that exceed that state of the art in the accuracy of unsupervised correspondences and accuracy of surface reconstruction.
arXiv Detail & Related papers (2021-04-14T16:21:22Z) - Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid
Representations [21.64457003420851]
We develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization.
We demonstrate that our method can be adopted to improve techniques for reconstructing neural implicit surfaces from multi-view images or point clouds.
arXiv Detail & Related papers (2020-12-11T15:51:04Z) - Deep Manifold Prior [37.725563645899584]
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent.
We show that surfaces generated this way are smooth, with limiting behavior characterized by Gaussian processes, and we mathematically derive such properties for fully-connected as well as convolutional networks.
arXiv Detail & Related papers (2020-04-08T20:47:56Z)
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.