DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus

• URL: http://arxiv.org/abs/2405.13943v2
• Date: Tue, 29 Oct 2024 13:42:40 GMT
• Title: DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
• Authors: Yu Chen, Gim Hee Lee,
• Abstract summary: We propose DoGaussian, a method that trains 3DGS distributedly. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes.
• Score: 56.45194233357833
• License:
• Abstract: The recent advances in 3D Gaussian Splatting (3DGS) show promising results on the novel view synthesis (NVS) task. With its superior rendering performance and high-fidelity rendering quality, 3DGS is excelling at its previous NeRF counterparts. The most recent 3DGS method focuses either on improving the instability of rendering efficiency or reducing the model size. On the other hand, the training efficiency of 3DGS on large-scale scenes has not gained much attention. In this work, we propose DoGaussian, a method that trains 3DGS distributedly. Our method first decomposes a scene into K blocks and then introduces the Alternating Direction Method of Multipliers (ADMM) into the training procedure of 3DGS. During training, our DOGS maintains one global 3DGS model on the master node and K local 3DGS models on the slave nodes. The K local 3DGS models are dropped after training and we only query the global 3DGS model during inference. The training time is reduced by scene decomposition, and the training convergence and stability are guaranteed through the consensus on the shared 3D Gaussians. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving state-of-the-art rendering quality. Our code is publicly available at https://github.com/AIBluefisher/DOGS.

Related papers

• 3DGS$^2$: Near Second-order Converging 3D Gaussian Splatting [26.94968605302451]
  3D Gaussian Splatting (3DGS) has emerged as a mainstream solution for novel view synthesis and 3D reconstruction. This paper introduces a (near) second-order convergent training algorithm for 3DGS, leveraging its unique properties.
  arXiv  Detail & Related papers  (2025-01-22T22:28:11Z)
• ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery [11.706262924395768]
  3D-GS often struggles to capture rich details and complete geometry. We introduce a novel densification method, residual split, which adds a downscaled Gaussian as a residual. Our approach is capable of adaptively retrieving details and complementing missing geometry while enabling progressive refinement.
  arXiv  Detail & Related papers  (2024-12-10T13:19:27Z)
• WildGaussians: 3D Gaussian Splatting in the Wild [80.5209105383932]
  We introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data.
  arXiv  Detail & Related papers  (2024-07-11T12:41:32Z)
• LP-3DGS: Learning to Prune 3D Gaussian Splatting [71.97762528812187]
  We propose learning-to-prune 3DGS, where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
  arXiv  Detail & Related papers  (2024-05-29T05:58:34Z)
• Bootstrap 3D Reconstructed Scenes from 3D Gaussian Splatting [10.06208115191838]
  We present a bootstrapping method to enhance the rendering of novel views using trained 3D-GS. Our results indicate that bootstrapping effectively reduces artifacts, as well as clear enhancements on the evaluation metrics.
  arXiv  Detail & Related papers  (2024-04-29T12:57:05Z)
• CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians [64.6687065215713]
  CityGaussian employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Our approach attains state-of-theart rendering quality, enabling consistent real-time rendering of largescale scenes across vastly different scales.
  arXiv  Detail & Related papers  (2024-04-01T14:24:40Z)
• 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)
• SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition [66.56357905500512]
  3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis. We propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS. Our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.
  arXiv  Detail & Related papers  (2024-01-31T14:19:03Z)

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.