GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion

• URL: http://arxiv.org/abs/2607.24403v1
• Date: Mon, 27 Jul 2026 13:20:26 GMT
• Title: GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion
• Abstract summary: We present a geometry-guided one-step generative decoding approach that jointly exploits decoded structural and appearance cues.<n>We also develop a three-stage optimization strategy that stabilizes the learning of the unified and adapts the generative decoder.
• Score: 18.74085761734125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feed-forward 3D Gaussian Splatting (3DGS) enables scalable scene reconstruction without per-scene optimization, yet produces dense Gaussians that are costly to store and transmit. Existing feed-forward Gaussian compression methods formulate decoding as deterministic representation recovery, which becomes inadequate at low bitrates when high-frequency textures and view-dependent appearance are discarded. Although generative models offer a promising alternative, using them as standalone post-processing decouples generation from the transmitted scene structure, thereby compromising cross-view consistency. To address these limitations, we propose GenSplatCodec, a unified feed-forward Gaussian codec that reformulates low-bitrate Gaussian compression as geometry-guided generative decoding. We present a detail-aware feed-forward Gaussian coding scheme within a dual-stream formulation, where the resulting compact Gaussian structural stream is complemented by a lightweight reference appearance stream. We further introduce a geometry-guided one-step generative decoding approach that jointly exploits decoded structural and appearance cues through hierarchical geometry control to reconstruct high-fidelity and view-consistent novel views. Finally, we develop a three-stage optimization strategy that stabilizes the learning of the unified codec and adapts the generative decoder to codec-derived structural and appearance cues. Extensive experiments across multiple datasets demonstrate that GenSplatCodec consistently achieves superior rate-distortion (RD) performance over existing methods.

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