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Computer Science > Computer Vision and Pattern Recognition

arXiv:2211.07082 (cs)
[Submitted on 14 Nov 2022]

Title:Learning Latent Part-Whole Hierarchies for Point Clouds

Authors:Xiang Gao, Wei Hu, Renjie Liao
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    Abstract: Strong evidence suggests that humans perceive the 3D
    world by parsing visual scenes and objects into part-whole
    hierarchies. Although deep neural networks have the capability of
    learning powerful multi-level representations, they can not
    explicitly model part-whole hierarchies, which limits their
    expressiveness and interpretability in processing 3D vision data
    such as point clouds. To this end, we propose an encoder-decoder
    style latent variable model that explicitly learns the part-whole
    hierarchies for the multi-level point cloud segmentation.
    Specifically, the encoder takes a point cloud as input and
    predicts the per-point latent subpart distribution at the middle
    level. The decoder takes the latent variable and the feature from
    the encoder as an input and predicts the per-point part
    distribution at the top level. During training, only annotated
    part labels at the top level are provided, thus making the whole
    framework weakly supervised. We explore two kinds of approximated
    inference algorithms, i.e., most-probable-latent and Monte Carlo
    methods, and three stochastic gradient estimations for learning
    discrete latent variables, i.e., straight-through, REINFORCE, and
    pathwise estimators. Experimental results on the PartNet dataset
    show that the proposed method achieves state-of-the-art
    performance in not only top-level part segmentation but also
    middle-level latent subpart segmentation.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2211.07082 [cs.CV]
          (or arXiv:2211.07082v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2211.07082
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Xiang Gao [view email]
[v1] Mon, 14 Nov 2022 03:17:33 UTC (8,202 KB)
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