Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting

• URL: http://arxiv.org/abs/2406.01593v1
• Date: Mon, 3 Jun 2024 17:59:51 GMT
• Title: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting
• Authors: Shaojie Ma, Yawei Luo, Yi Yang,
• Abstract summary: This paper introduces the Mesh-adsorbed Gaussian Splatting (MaGS) method to resolve such a dilemma. MaGS constrains 3D Gaussians to hover on the mesh surface, creating a mutual-adsorbed mesh-Gaussian 3D representation. By joint optimizing meshes, 3D Gaussians, and RDF, MaGS achieves both high rendering accuracy and realistic deformation.
• Score: 26.382349137191547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D reconstruction and simulation, while interrelated, have distinct objectives: reconstruction demands a flexible 3D representation adaptable to diverse scenes, whereas simulation requires a structured representation to model motion principles effectively. This paper introduces the Mesh-adsorbed Gaussian Splatting (MaGS) method to resolve such a dilemma. MaGS constrains 3D Gaussians to hover on the mesh surface, creating a mutual-adsorbed mesh-Gaussian 3D representation that combines the rendering flexibility of 3D Gaussians with the spatial coherence of meshes. Leveraging this representation, we introduce a learnable Relative Deformation Field (RDF) to model the relative displacement between the mesh and 3D Gaussians, extending traditional mesh-driven deformation paradigms that only rely on ARAP prior, thus capturing the motion of each 3D Gaussian more precisely. By joint optimizing meshes, 3D Gaussians, and RDF, MaGS achieves both high rendering accuracy and realistic deformation. Extensive experiments on the D-NeRF and NeRF-DS datasets demonstrate that MaGS can generate competitive results in both reconstruction and simulation.

Related papers

• MeshXL: Neural Coordinate Field for Generative 3D Foundation Models [51.1972329762843]
  We present a family of generative pre-trained auto-regressive models, which addresses the process of 3D mesh generation with modern large language model approaches. MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.
  arXiv  Detail & Related papers  (2024-05-31T14:35:35Z)
• GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction [3.043712258792239]
  We present a unified framework integrating neural SDF with 3DGS. This framework incorporates a learnable neural SDF field to guide the densification and pruning of Gaussians. Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-05-30T03:46:59Z)
• Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and compact surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• 3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting [58.95801720309658]
  In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR. The key insight is incorporating an implicit signed distance field (SDF) within 3D Gaussians to enable them to be aligned and jointly optimized. Our experimental results demonstrate that our 3DGSR method enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS.
  arXiv  Detail & Related papers  (2024-03-30T16:35:38Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis [90.26556260531707]
  DMTet is a conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. Unlike deep 3D generative models that directly generate explicit representations such as meshes, our model can synthesize shapes with arbitrary topology.
  arXiv  Detail & Related papers  (2021-11-08T05:29:35Z)

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.