优化最小化四维高斯散射

摘要

4D高斯溅射作为一种动态场景表示的新范式，能够实现复杂运动场景的实时渲染。然而，它面临着一个主要挑战——存储开销问题，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，但在压缩率或视觉质量方面仍存在局限。在本研究中，我们提出了OMG4（优化的最小4D高斯溅射），该框架构建了一组紧凑的显著高斯分布，能够忠实表示4D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) 高斯合并，融合具有相似特性的基元。此外，我们集成了隐式外观压缩，并将子向量量化（SVQ）推广至4D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显著优于最新的先进方法，在保持重建质量的同时，模型大小减少了60%以上。这些成果标志着OMG4在紧凑4D场景表示方面迈出了重要一步，为广泛的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/.