MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization

• URL: http://arxiv.org/abs/2502.07754v1
• Date: Tue, 11 Feb 2025 18:27:39 GMT
• Title: MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization
• Authors: Rafał Tobiasz, Grzegorz Wilczyński, Marcin Mazur, Sławomir Tadeja, Przemysław Spurek,
• Abstract summary: We introduce MeshSplats, a method which converts Gaussian elements into mesh faces. Our model can be utilized immediately following transformation, yielding a mesh of slightly reduced quality without additional training.
• Score: 0.4543820534430523
• License:
• Abstract: Gaussian Splatting (GS) is a recent and pivotal technique in 3D computer graphics. GS-based algorithms almost always bypass classical methods such as ray tracing, which offers numerous inherent advantages for rendering. For example, ray tracing is able to handle incoherent rays for advanced lighting effects, including shadows and reflections. To address this limitation, we introduce MeshSplats, a method which converts GS to a mesh-like format. Following the completion of training, MeshSplats transforms Gaussian elements into mesh faces, enabling rendering using ray tracing methods with all their associated benefits. Our model can be utilized immediately following transformation, yielding a mesh of slightly reduced quality without additional training. Furthermore, we can enhance the reconstruction quality through the application of a dedicated optimization algorithm that operates on mesh faces rather than Gaussian components. The efficacy of our method is substantiated by experimental results, underscoring its extensive applications in computer graphics and image processing.

Related papers

• 3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering [8.59572577251833]
  We propose a novel regularization method using the gradients of a signed distance function estimated from the Gaussians. We demonstrate the effectiveness of our approach on datasets such as Mip-NeRF360, Tanks and Temples, and Deep-Blending.
  arXiv  Detail & Related papers  (2025-01-14T18:40:33Z)
• GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering [83.69136534797686]
  We present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated scenes featuring rough and glossy surfaces. This paper starts by analyzing and comparing two prominent shading techniques popularly used for inverse rendering, forward shading and deferred shading. We propose a unified shading solution that combines the advantages of both techniques for better decomposition.
  arXiv  Detail & Related papers  (2024-11-12T01:51:05Z)
• GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering [6.820642721852439]
  We present GI-GS, a novel inverse rendering framework that leverages 3D Gaussian Splatting (3DGS) and deferred shading. In our framework, we first render a G-buffer to capture the detailed geometry and material properties of the scene. With the G-buffer and previous rendering results, the indirect lighting can be calculated through a lightweight path tracing.
  arXiv  Detail & Related papers  (2024-10-03T15:58:18Z)
• DiffCSG: Differentiable CSG via Rasterization [42.48127201787373]
  Differentiable rendering is a key ingredient for inverse rendering and machine learning. We present an algorithm, DiffCSG, to render ConstructiveSolidGeometry (CSG) in a differentiable manner. Our algorithm is simple and fast, can be easily incorporated into modern machine learning setups, and enables a range of applications.
  arXiv  Detail & Related papers  (2024-09-02T18:57:07Z)
• DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading [50.331929164207324]
  We introduce DeferredGS, a method for decoupling and editing the Gaussian splatting representation using deferred shading. Both qualitative and quantitative experiments demonstrate the superior performance of DeferredGS in novel view and editing tasks.
  arXiv  Detail & Related papers  (2024-04-15T01:58:54Z)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)
• GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization [62.13932669494098]
  This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to factorize the scene into material properties, light, and geometry. We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision. We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport.
  arXiv  Detail & Related papers  (2023-12-08T16:05:15Z)
• GS-IR: 3D Gaussian Splatting for Inverse Rendering [71.14234327414086]
  We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) We extend GS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction, photorealistic novel view synthesis, and effective physically-based rendering.
  arXiv  Detail & Related papers  (2023-11-26T02:35:09Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)

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.