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Computer Science > Computer Vision and Pattern Recognition

arXiv:2209.07619 (cs)
[Submitted on 15 Sep 2022]

Title:Hierarchical Superquadric Decomposition with Implicit Space Separation

Authors:Jaka Šircelj, Peter Peer, Franc Solina, Vitomir Štruc
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Abstract:We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2209.07619 [cs.CV]
  (or arXiv:2209.07619v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2209.07619

Submission history

From: Jaka Šircelj [view email]
[v1] Thu, 15 Sep 2022 21:34:46 UTC (3,870 KB)
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