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arXiv Computer Vision

LoBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction

LoBE-GS is a Load‑Balanced and Efficient 3D Gaussian Splatting framework designed for large‑scale scene reconstruction. It introduces a load‑balanced KD‑tree partitioning scheme that balances camera counts per block, uses depth‑based back‑projection to speed up camera assignment, and applies visibility cropping and selective densification to cut training costs. Experiments on urban and outdoor datasets show up to an order‑of‑magnitude faster scene‑partitioning and a 3.2× reduction in worst‑block fine‑training time compared to CityGaussian, while keeping reconstruction quality competitive.

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arXiv Computer Vision
Aug 28

KISS-GS: 3D Gaussian Splatting Compression Kept Simple

KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.

By Wieland Morgenstern, Friedrich Elias Branschke, Florian Fleischmann, Adrian Szatmari, Paul Schlack, Florian Barthel, Peter Eisert, Anna Hilsmann
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arXiv Computer Vision
Oct 5

Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD

Budgeted-GS is a post‑hoc technique that transforms any trained 3D Gaussian Splatting model into a factoring tree—a multi‑resolution hierarchy of moment‑matched aggregates. After a brief construction pass, a single quality parameter selects the appropriate level of detail for each view, allowing the same city‑scale model to run in real time on GPUs with varying memory capacities. The method also informs budget‑centered training, enabling models to be built directly at the necessary size and avoiding wasted optimization of later discarded primitives.

By Haipeng Wang
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Hugging Face Trending Papers
Jul 9

Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction

Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.

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Hugging Face Trending Papers
Jun 17

Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication

3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gaussians across multiple GPUs. Existing distributed approaches either partition scenes into isolated regions, causing global inconsistency, or rely on global Gaussian-level exchanges, which lead to substantial growth in inter-GPU communication and quickly dominate iteration time.

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arXiv Computer Vision
Sep 3

PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

PointGauss is a 3D-native framework that performs semantic parsing and instance segmentation on 3D Gaussian splatting representations by treating Gaussian primitives as unstructured point sets and extracting scale‑invariant geometric features with Point Transformer V3. It introduces an adaptive region‑of‑interest cropping strategy and an instance‑aware distance‑constrained rasterization pipeline to enable scalable, view‑consistent pixel‑level projections. The authors also release SplatSeg‑360, a cross‑scale benchmark with 32 complex scenes and over 6,300 aligned 2D‑3D masks, and show that PointGauss achieves real‑time performance with state‑of‑the‑art 3D‑mIoU (~90%) and 2D‑mIoU (~80%) scores.

By Wentao Sun, Yiping Chen, John S. Zelek, Jonathan Li
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