{"ID":2834872,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00850","arxiv_id":"2512.00850","title":"Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting","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.","short_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 enco...","url_abs":"https://arxiv.org/abs/2512.00850","url_pdf":"https://arxiv.org/pdf/2512.00850v2","authors":"[\"Haishan Wang\",\"Mohammad Hassan Vali\",\"Arno Solin\"]","published":"2025-11-30T11:42:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}