GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
- URL: http://arxiv.org/abs/2404.01810v2
- Date: Wed, 17 Jul 2024 13:58:34 GMT
- Title: GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
- Authors: Yaniv Wolf, Amit Bracha, Ron Kimmel,
- Abstract summary: 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes.
We propose a novel approach for bridging the gap between the noisy 3DGS representation and the smooth 3D mesh representation.
We render stereo-aligned pairs of images corresponding to the original training poses, feed the pairs into a stereo model to get a depth profile, and finally fuse all of the profiles together to get a single mesh.
- Score: 9.175560202201819
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes. However, despite its superior novel view synthesis capabilities, extracting the geometry of the scene directly from the Gaussian properties remains a challenge, as those are optimized based on a photometric loss. While some concurrent models have tried adding geometric constraints during the Gaussian optimization process, they still produce noisy, unrealistic surfaces. We propose a novel approach for bridging the gap between the noisy 3DGS representation and the smooth 3D mesh representation, by injecting real-world knowledge into the depth extraction process. Instead of extracting the geometry of the scene directly from the Gaussian properties, we instead extract the geometry through a pre-trained stereo-matching model. We render stereo-aligned pairs of images corresponding to the original training poses, feed the pairs into a stereo model to get a depth profile, and finally fuse all of the profiles together to get a single mesh. The resulting reconstruction is smoother, more accurate and shows more intricate details compared to other methods for surface reconstruction from Gaussian Splatting, while only requiring a small overhead on top of the fairly short 3DGS optimization process. We performed extensive testing of the proposed method on in-the-wild scenes, obtained using a smartphone, showcasing its superior reconstruction abilities. Additionally, we tested the method on the Tanks and Temples and DTU benchmarks, achieving state-of-the-art results.
Related papers
- RaDe-GS: Rasterizing Depth in Gaussian Splatting [32.38730602146176]
Gaussian Splatting (GS) has proven to be highly effective in novel view synthesis, achieving high-quality and real-time rendering.
Our work introduces a Chamfer distance error comparable to NeuraLangelo on the DTU dataset and maintains similar computational efficiency as the original 3D GS methods.
arXiv Detail & Related papers (2024-06-03T15:56:58Z) - SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene.
SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
arXiv Detail & Related papers (2024-04-29T23:26:30Z) - 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) - SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface [7.052369521411523]
We present SplatFace, a novel Gaussian splatting framework designed for 3D human face reconstruction without reliance on accurate pre-determined geometry.
Our method is designed to simultaneously deliver both high-quality novel view rendering and accurate 3D mesh reconstructions.
arXiv Detail & Related papers (2024-03-27T17:32:04Z) - Bridging 3D Gaussian and Mesh for Freeview Video Rendering [57.21847030980905]
GauMesh bridges the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes.
We show that our approach adapts the appropriate type of primitives to represent the different parts of the dynamic scene.
arXiv Detail & Related papers (2024-03-18T04:01:26Z) - Compact 3D Gaussian Splatting For Dense Visual SLAM [26.47738770606461]
We propose a compact 3D Gaussian Splatting SLAM system that reduces the number and the parameter size of Gaussian ellipsoids.
A sliding window-based masking strategy is first proposed to reduce the redundant ellipsoids.
Our method achieves faster training and rendering speed while maintaining the state-of-the-art (SOTA) quality of the scene representation.
arXiv Detail & Related papers (2024-03-17T15:41:35Z) - 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) - GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner.
The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
arXiv Detail & Related papers (2023-12-04T18:59:55Z) - 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.