AI RESEARCH
Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting
arXiv CS.CV
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ArXi:2512.00850v2 Announce Type: replace 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.