Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations
Yushi Du (Peking University),* Ruihai Wu (Peking University), Yan Shen (Peking University), Hao Dong (Peking University)
The 34th British Machine Vision Conference
Abstract
Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom, and are rich in geometries, semantics and part functions. Modeling different kinds of parts and articulations plays an essential role in articulated object understanding and manipulation, and will further benefit 3D vision and robotics communities. To model articulated objects, most previous works directly encode articulated objects into feature representations, without specific designs for parts, articulations and part motions. In this paper, we introduce a novel framework that disentangles the part motion of articulated objects by predicting the transformation matrix of points on the part surface, using spatially continuous neural implicit representations to model the part motion smoothly in the space. Besides, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds motions in the space. Quantitative and qualitative results of experiments over diverse categories of articulated objects demonstrate the effectiveness of our proposed framework.Video
Citation
@inproceedings{Du_2023_BMVC,
author = {Yushi Du and Ruihai Wu and Yan Shen and Hao Dong},
title = {Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0304.pdf}
}
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