透過高斯潑濺技術構建複雜關節物體的互動式複製品

Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting

February 26, 2025

作者: Yu Liu, Baoxiong Jia, Ruijie Lu, Junfeng Ni, Song-Chun Zhu, Siyuan Huang

cs.AI

摘要

構建關節化物體是計算機視覺領域的一個關鍵挑戰。現有方法往往無法有效地整合不同物體狀態之間的信息，這限制了部件網格重建和部件動力學建模的準確性，尤其是在處理複雜的多部件關節化物體時。我們提出了ArtGS，這是一種新穎的方法，利用3D高斯作為靈活且高效的表示來解決這些問題。我們的方法結合了規範高斯與從粗到細的初始化和更新策略，以對齊不同物體狀態下的關節部件信息，並採用了一個受蒙皮啟發的部件動力學建模模塊，以提升部件網格重建和關節學習的效果。在合成和真實世界數據集上的大量實驗，包括一個針對複雜多部件物體的新基準測試，表明ArtGS在聯合參數估計和部件網格重建方面達到了最先進的性能。我們的方法顯著提高了重建質量和效率，尤其是在處理多部件關節化物體時。此外，我們還提供了對設計選擇的全面分析，驗證了每個組件的有效性，並指出了未來改進的潛在方向。

English

Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics modeling, particularly for complex multi-part articulated objects. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation to address these issues. Our method incorporates canonical Gaussians with coarse-to-fine initialization and updates for aligning articulated part information across different object states, and employs a skinning-inspired part dynamics modeling module to improve both part-mesh reconstruction and articulation learning. Extensive experiments on both synthetic and real-world datasets, including a new benchmark for complex multi-part objects, demonstrate that ArtGS achieves state-of-the-art performance in joint parameter estimation and part mesh reconstruction. Our approach significantly improves reconstruction quality and efficiency, especially for multi-part articulated objects. Additionally, we provide comprehensive analyses of our design choices, validating the effectiveness of each component to highlight potential areas for future improvement.