基于高斯溅射构建复杂铰接物体的可交互复制体
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
构建关节物体是计算机视觉领域的一项关键挑战。现有方法往往难以有效整合不同物体状态间的信息,限制了部件网格重建和部件动态建模的准确性,尤其对于复杂的多部件关节物体而言。我们提出了ArtGS,一种新颖的方法,利用3D高斯作为灵活且高效的表示来解决这些问题。我们的方法结合了规范高斯与从粗到细的初始化和更新策略,以对齐不同物体状态下的关节部件信息,并采用了一种受蒙皮启发的部件动态建模模块,以提升部件网格重建和关节学习的效果。在合成和真实世界数据集上的大量实验,包括针对复杂多部件物体的新基准测试,均表明ArtGS在联合参数估计和部件网格重建方面达到了最先进的性能。我们的方法显著提高了重建质量和效率,特别是对于多部件关节物体。此外,我们还提供了对设计选择的全面分析,验证了每个组件的有效性,并指出了未来改进的潜在方向。
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.Summary
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