← 研踪 PaperTrailarXiv:2510.03857 · v2
Optimized Minimal 4D Gaussian Splatting for Efficient Dynamic Scene Representation
首次提交 2025-10-05 00:11
版本更新 2026-11-12 02:39
雷达观测 2026-11-13 14:16
摘要
4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. Compressing explicit 4D Gaussians is challenging because they exhibit heterogeneous redundancy across static and dynamic regions, and their temporal attributes make direct quantization unstable. In this work, we present OMG4 (Optimized Minimal 4D Gaussian Splatting), a framework that constructs a compact set of salient Gaussians capable of faithfully representing 4D Gaussian models. Our method progressively reduces Gaussians in three stages: (1) Gaussian Sampling to identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning to remove redundancies, and (3) Gaussian Merging to fuse primitives with similar characteristics. In addition, we integrate implicit appearance compression and extend Sub-Vector Quantization (SVQ) to 4D representations with a staged quantization scheme, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 achieves a favorable rate-distortion trade-off over recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results demonstrate the effectiveness of OMG4 as a practical framework for compact and high-fidelity 4D scene representation. Our source code is available at https://minshirley.github.io/OMG4/.
作者备注：23 pages, 10 figures
观测到的版本
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v2 · 2026-11-12 02:39 · 最新观测版本
Optimized Minimal 4D Gaussian Splatting for Efficient Dynamic Scene Representation
4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. Compressing explicit 4D Gaussians is challenging because they exhibit heterogeneous redundancy across static and dynamic regions, and their temporal attributes make direct quantization unstable. In this work, we present OMG4 (Optimized Minimal 4D Gaussian Splatting), a framework that constructs a compact set of salient Gaussians capable of faithfully representing 4D Gaussian models. Our method progressively reduces Gaussians in three stages: (1) Gaussian Sampling to identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning to remove redundancies, and (3) Gaussian Merging to fuse primitives with similar characteristics. In addition, we integrate implicit appearance compression and extend Sub-Vector Quantization (SVQ) to 4D representations with a staged quantization scheme, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 achieves a favorable rate-distortion trade-off over recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results demonstrate the effectiveness of OMG4 as a practical framework for compact and high-fidelity 4D scene representation. Our source code is available at https://minshirley.github.io/OMG4/.
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