GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations

• URL: http://arxiv.org/abs/2503.00807v1
• Date: Sun, 02 Mar 2025 09:17:08 GMT
• Title: GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations
• Authors: Yuezhi Yang, Haitao Yang, Kiyohiro Nakayama, Xiangru Huang, Leonidas Guibas, Qixing Huang,
• Abstract summary: GenAnalysis is an implicit shape generation framework that allows joint analysis of man-made shapes.<n>We show how to extract shape variations by recovering piecewise affine vector fields in the tangent space of each shape.<n>We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes.
• Score: 21.923143529947886
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present GenAnalysis, an implicit shape generation framework that allows joint analysis of man-made shapes, including shape matching and joint shape segmentation. The key idea is to enforce an as-affine-as-possible (AAAP) deformation between synthetic shapes of the implicit generator that are close to each other in the latent space, which we achieve by designing a regularization loss. It allows us to understand the shape variation of each shape in the context of neighboring shapes and also offers structure-preserving interpolations between the input shapes. We show how to extract these shape variations by recovering piecewise affine vector fields in the tangent space of each shape. These vector fields provide single-shape segmentation cues. We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes. These correspondences are then used to aggregate single-shape segmentation cues into consistent segmentations. We conduct experiments on the ShapeNet dataset to show superior performance in shape matching and joint shape segmentation over previous methods.

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