MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo

• URL: http://arxiv.org/abs/2405.12218v3
• Date: Mon, 15 Jul 2024 12:34:30 GMT
• Title: MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
• Authors: Tianqi Liu, Guangcong Wang, Shoukang Hu, Liao Shen, Xinyi Ye, Yuhang Zang, Zhiguo Cao, Wei Li, Ziwei Liu,
• Abstract summary: We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) MVSGaussian achieves real-time rendering with better synthesis quality for each scene.
• Score: 54.00987996368157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes. Specifically, 1) we leverage MVS to encode geometry-aware Gaussian representations and decode them into Gaussian parameters. 2) To further enhance performance, we propose a hybrid Gaussian rendering that integrates an efficient volume rendering design for novel view synthesis. 3) To support fast fine-tuning for specific scenes, we introduce a multi-view geometric consistent aggregation strategy to effectively aggregate the point clouds generated by the generalizable model, serving as the initialization for per-scene optimization. Compared with previous generalizable NeRF-based methods, which typically require minutes of fine-tuning and seconds of rendering per image, MVSGaussian achieves real-time rendering with better synthesis quality for each scene. Compared with the vanilla 3D-GS, MVSGaussian achieves better view synthesis with less training computational cost. Extensive experiments on DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples datasets validate that MVSGaussian attains state-of-the-art performance with convincing generalizability, real-time rendering speed, and fast per-scene optimization.

Related papers

• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [73.49548565633123]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images. We propose a view framework based on 3D Gaussian Splatting, named MCGS, enabling scene reconstruction from sparse input views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis [22.80370814838661]
  Recent works in volume rendering, textite.g. NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency. We propose a new 3DGS optimization method embodying four key novel contributions.
  arXiv  Detail & Related papers  (2024-10-02T23:48:31Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• 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)
• FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting [58.41056963451056]
  We propose a few-shot view synthesis framework based on 3D Gaussian Splatting. This framework enables real-time and photo-realistic view synthesis with as few as three training views. FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets.
  arXiv  Detail & Related papers  (2023-12-01T09:30:02Z)
• 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.