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2026-02-27 14:10:48
raw text
Neural Shape Mating Neural Shape Mating Home Dataset Experiments Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors Yun-Chun Chen 1,2 Haoda Li 1,2 Dylan Turpin 1,2 Alec Jacobson 1,4 Animesh Garg 1,2,3 1 University of Toronto 2 Vector Institute 3 NVIDIA 4 Adobe Research, Toronto IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Paper Video Code Abstract Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit. By focusing on shape alignment rather than semantic cues, we can achieve across category generalization and scaling. In this paper, we introduce a novel...
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