arXiv Computer Vision
Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression
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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.
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Codec-Compatible Learned Compression for 3D Gaussian Splatting
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Towards Practical Compression of 3D Gaussian Splatting
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arXiv Machine Learning
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arXiv AI
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VLCQ: Post-training quantization for deep neural networks using variable length coding
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