Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting

TL;DR

Smol-GS introduces a compact, efficient method for 3D Gaussian Splatting that significantly reduces storage needs while maintaining high rendering quality through novel encoding and compression techniques.

Contribution

It proposes a new approach combining octree-based positional encoding and entropy compression to achieve unprecedented storage efficiency in 3D Gaussian Splatting.

Findings

Achieves orders-of-magnitude storage reduction.

Maintains high rendering quality on standard benchmarks.

Uses octree-derived encoding and entropy-based compression.

Abstract

We present Smol-GS, a novel method for learning compact representations for 3D Gaussian Splatting (3DGS). Our approach learns highly efficient splat-wise features to model 3D space which capture abstracted cues, including color, opacity, transformation, and material properties. We propose octree-derived positional encoding, which explicitly models spatial locality and enhances representation efficiency. We further apply entropy-based compression to exploit feature redundancy, and compress splat coordinates using a recursive voxel hierarchy. This design enables orders-of-magnitude storage reduction while preserving representation flexibility. Smol-GS achieves state-of-the-art compression performance on standard benchmarks with high-level rendering quality.