Pith. sign in

REVIEW 7 cited by

VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.17427 v1 pith:BQJXPI35 submitted 2024-02-27 cs.CV

Jiaqi Lin , Zhihao Li , Xiao Tang , Jianzhuang Liu , Shiyong Liu , Jiayue Liu , Yangdi Lu , Xiaofei Wu
show 3 more authors
Songcen Xu Youliang Yan Wenming Yang
This is my paper · ORCID
classification cs.CV
keywords largesceneoptimizationappearancereconstructionrenderingscenescells
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ESVR: 3D Ellipsoid-based Sparse Volume Rendering via Structure-aware Primitive Learning and Per-primitive Ray Sampling

    cs.GR 2026-08 conditional novelty 7.0 of 10

    A method that fits differentiable ellipsoids to sparse volumetric intensity fields and renders them via per-primitive ray sampling, achieving real-time interaction for hundreds of gigabytes of data at high compression.

  2. 3D Gaussian Splatting for Scientific Particle Data Compression and Rendering

    cs.GR 2026-07 conditional novelty 6.0 of 10

    ParticleGS uses 3D Gaussian splats to mimic ParaView renderings of 281M-particle data, reaching 30 dB PSNR at 65x compression and rendering at 662 FPS.

  3. GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A camera-only Gaussian-surfel pipeline reconstructs full Waymo scenes, converts them to binary occupancy labels, and trains CVT-Occ to generalize on Occ3D-Waymo and Occ3D-nuScenes at a level close to or above LiDAR-la...

  4. Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dual-branch optimization fitting body motion and per-video 3D Gaussian appearance jointly, guided by a diffusion proxemics prior, improves close-interaction reconstruction from monocular video.

  5. Virtual Memory for 3D Gaussian Splatting

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A proxy-mesh visibility buffer with page streaming and level of detail lets 3D Gaussian Splatting render scenes larger than GPU memory while culling occluded Gaussians.

  6. Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A proxy mesh rendered through hardware rasterization provides a cheap occlusion depth prior that culls hidden anchors at inference and guides densification at training, giving Octree-GS-like MLP splatting a 3 to 4x sp...

  7. Hi^2-GSLoc: Dual-Hierarchical Gaussian-Specific Visual Relocalization for Remote Sensing

    cs.CV 2025-07 reject novelty 5.0 of 10

    A 3DGS-based aerial visual relocalization pipeline reports strong accuracy, but its 100 percent recall after filtering is achieved by excluding hard queries from the evaluation.

Pith tools