用自监督分解+4D哈希编码，实现实时动态场景高质量渲染
DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Rendering
- 通过自监督分解分离动态与静态部分，无需人工标注
- 采用多分辨率4D哈希编码，避免低秩假设导致的特征重叠
- 在真实数据集上达264帧/秒，视觉质量优于现有方法
动态场景重建是3D视觉中的长期挑战。现有的基于平面的动态高斯点阵方法因低秩假设不当，导致特征重叠和渲染质量差。尽管4D哈希编码能提供无低秩约束的显式表示，但直接应用于整个动态场景会造成大量哈希冲突和冗余。为此，我们提出DASH，一个实时动态场景渲染框架，结合4D哈希编码与自监督分解。方法首先通过自监督分解机制，在无手动标注或预计算掩码的情况下分离动态与静态成分；接着引入多分辨率4D哈希编码器处理动态元素，实现避免低秩假设的显式表示；最后提出时空平滑正则化策略，缓解不稳定的形变伪影。在真实世界数据集上的实验表明，DASH实现了最先进的动态渲染性能，在单块4090显卡上达到264 FPS的实时速度，同时显著提升视觉质量。
原文摘要 · Abstract (English)
Dynamic scene reconstruction is a long-term challenge in 3D vision. Existing plane-based methods in dynamic Gaussian splatting suffer from an unsuitable low-rank assumption, causing feature overlap and poor rendering quality. Although 4D hash encoding provides an explicit representation without low-rank constraints, directly applying it to the entire dynamic scene leads to substantial hash collisions and redundancy. To address these challenges, we present DASH, a real-time dynamic scene rendering framework that employs 4D hash encoding coupled with self-supervised decomposition. Our approach begins with a self-supervised decomposition mechanism that separates dynamic and static components without manual annotations or precomputed masks. Next, we introduce a multiresolution 4D hash encoder for dynamic elements, providing an explicit representation that avoids the low-rank assumption. Finally, we present a spatio-temporal smoothness regularization strategy to mitigate unstable deformation artifacts. Experiments on real-world datasets demonstrate that DASH achieves state-of-the-art dynamic rendering performance, exhibiting enhanced visual quality at real-time speeds of 264 FPS on a single 4090 GPU. Code: https://github.com/chenj02/DASH.
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