優化的最小四維高斯散點法

摘要

四维高斯溅射（4D Gaussian Splatting）作为一种动态场景表示的新范式，能够实现对复杂运动场景的实时渲染。然而，其面临的主要挑战在于存储开销，因为高保真重建需要数百万个高斯分布。尽管已有若干研究尝试减轻这一内存负担，但在压缩比或视觉质量方面仍存在局限。本研究提出了OMG4（优化的最小四维高斯溅射框架），该框架构建了一组紧凑的关键高斯分布，能够忠实表示四维高斯模型。我们的方法通过三个阶段逐步修剪高斯分布：（1）高斯采样，识别对重建保真度至关重要的基元；（2）高斯修剪，去除冗余；（3）高斯融合，合并具有相似特性的基元。此外，我们整合了隐式外观压缩，并将子向量量化（SVQ）推广至四维表示，在保持质量的同时进一步减少存储需求。在标准基准数据集上的大量实验表明，OMG4显著优于近期的最先进方法，模型尺寸减少超过60%，同时保持了重建质量。这些成果标志着OMG4在紧凑四维场景表示领域迈出了重要一步，为广泛应用开辟了新的可能性。我们的源代码可在https://minshirley.github.io/OMG4/获取。

English

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. While several studies have attempted to alleviate this memory burden, they still face limitations in compression ratio or visual quality. 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 prunes 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 generalize Sub-Vector Quantization (SVQ) to 4D representations, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 significantly outperforms recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results position OMG4 as a significant step forward in compact 4D scene representation, opening new possibilities for a wide range of applications. Our source code is available at https://minshirley.github.io/OMG4/.