高效高斯泼溅用于单目动态场景渲染：基于稀疏时变属性建模
Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via Sparse Time-Variant Attribute Modeling
AI总结：
针对单目动态场景渲染中高斯冗余导致速度慢的问题，提出高效动态高斯泼溅（EDGS），通过稀疏时变属性建模与无监督静态锚点过滤，显著提升渲染速度并保持高质量。
摘要：
从单目视频渲染动态场景是一项至关重要但具有挑战性的任务。近年来，可变形高斯泼溅已成为表示真实世界动态场景的稳健解决方案。然而，它常常导致大量冗余的高斯，试图拟合不同时间步的每个训练视图，从而降低渲染速度。此外，静态区域中高斯的属性是时不变的，因此无需对每个高斯进行建模，这可能导致静态区域出现抖动。实际上，动态场景渲染速度的主要瓶颈在于高斯的数量。为此，我们引入了高效动态高斯泼溅（EDGS），通过稀疏时变属性建模来表示动态场景。我们的方法使用稀疏锚点网格表示来构建动态场景，并通过经典核表示计算密集高斯的运动流。此外，我们提出了一种无监督策略，以高效过滤掉对应静态区域的锚点。只有与可变形对象相关的锚点才被输入到多层感知机（MLP）中查询时变属性。在两个真实世界数据集上的实验表明，与先前最先进的方法相比，我们的EDGS显著提高了渲染速度，同时具有更优的渲染质量。
英文摘要：
Rendering dynamic scenes from monocular videos is a crucial yet challenging task. The recent deformable Gaussian Splatting has emerged as a robust solution to represent real-world dynamic scenes. However, it often leads to heavily redundant Gaussians, attempting to fit every training view at various time steps, leading to slower rendering speeds. Additionally, the attributes of Gaussians in static areas are time-invariant, making it unnecessary to model every Gaussian, which can cause jittering in static regions. In practice, the primary bottleneck in rendering speed for dynamic scenes is the number of Gaussians. In response, we introduce Efficient Dynamic Gaussian Splatting (EDGS), which represents dynamic scenes via sparse time-variant attribute modeling. Our approach formulates dynamic scenes using a sparse anchor-grid representation, with the motion flow of dense Gaussians calculated via a classical kernel representation. Furthermore, we propose an unsupervised strategy to efficiently filter out anchors corresponding to static areas. Only anchors associated with deformable objects are input into MLPs to query time-variant attributes. Experiments on two real-world datasets demonstrate that our EDGS significantly improves the rendering speed with superior rendering quality compared to previous state-of-the-art methods.
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