透過稀疏時間變異屬性建模，實現單眼動態場景渲染的高效高斯點降解

Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via Sparse Time-Variant Attribute Modeling

February 27, 2025

作者: Hanyang Kong, Xingyi Yang, Xinchao Wang

cs.AI

摘要

從單眼視頻中呈現動態場景是一項至關重要但具有挑戰性的任務。最近出現的可變形高斯飛濺技術已成為代表真實世界動態場景的堅固解決方案。然而，它常常導致高度冗餘的高斯函數，試圖在不同時間步驟擬合每個訓練視圖，進而導致較慢的呈現速度。此外，靜態區域中的高斯函數屬性是時間不變的，因此無需對每個高斯函數進行建模，這可能導致靜態區域出現抖動。在實踐中，動態場景呈現速度的主要瓶頸是高斯函數的數量。為此，我們提出了高效動態高斯飛濺（EDGS），通過稀疏的時間變化屬性建模來表示動態場景。我們的方法使用稀疏錨點網格表示動態場景，通過經典核函數表示計算密集高斯函數的運動流。此外，我們提出了一種無監督策略，以有效地過濾與靜態區域相對應的錨點。僅將與可變形對象相關的錨點輸入到MLP中以查詢時間變化屬性。在兩個真實世界數據集上的實驗表明，我們的EDGS相對於先前最先進的方法，顯著提高了呈現速度並具有優越的呈現質量。

English

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.