arXiv Computer Vision
Beyond UV Mapping: Mesh Texture Compression via Surface-Aligned Texture Fields
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arXiv Computer Vision
Sep 3
CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting
CC-4DGS introduces a storage‑efficient framework for dynamic 4D Gaussian splatting by replacing large multi‑resolution hash tables with a deterministic dense hash encoding and compact neural decoders, reducing deformation storage to 1–3 MB per scene. It also compresses canonical point‑cloud attributes through conditional autoencoding, selective quantization, and residual codebooks, achieving 3–5× reduction in point‑cloud size with negligible quality loss. The combined approach maintains real‑time rendering performance while lowering total storage to 20–30 MB, matching state‑of‑the‑art reconstruction accuracy on N3DV and Technicolor Light Field datasets.
arXiv Machine Learning
5d ago
Explicit 3D Gaussian Models for Fast, Mesh-Free Compression of Structured and Unstructured Volumes
The paper presents an explicit compression method for structured and unstructured volume data using 3D Gaussian primitives. By treating collections of Gaussians as an explicit scalar field representation, the authors develop CUDA‑accelerated sampling pipelines and loss functions that ensure accurate domain coverage, eliminating the need for mesh storage in unstructured volumes. The approach achieves faster training, higher reconstruction quality, and significantly higher compression ratios compared to existing implicit neural representations.
arXiv Computer Vision
Aug 31
Non-Uniform Quantisation for 3DGS Compression
The paper introduces a non‑uniform quantisation scheme designed for 3D Gaussian Splatting (3DGS) models, addressing their high bitrate demands. By applying importance‑weighted quantisation and merging, the method adapts to the data distribution and removes post‑voxelisation redundancy. Experiments on benchmark datasets show state‑of‑the‑art compression performance, and the scheme is compatible with any point‑cloud representation, positioning it as a candidate for future MPEG 3DGS standardisation.
arXiv Machine Learning
Jul 2
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
arXiv Computer Vision
Oct 1
Bake It Till You Make It: Ultrafast Spatial Texture-Atlas Splatting
arXiv:2607.13808v2 Announce Type: replace Abstract: Neural radiance representations in Gaussian Splatting (GS) deliver high-fidelity color detail but impose substantial rendering overhead from networ...
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.