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arXiv Computer Vision By Ruchao Bao, Wenzheng Wu, Chucheng Xiang, Zhongyuan Liu, Yuan Liu, Jinxin Dong, Ligang Liu, Ziqi Wang

PART: Learning 3D Part Assembly and Retrieval with Transformers

Read the original on arXiv Computer Vision →

PART is a transformer-based framework that performs 3D part retrieval and assembly by selecting appropriate parts from a library and predicting their 6‑DoF poses to reconstruct a target shape. It tackles the combinatorial search space, variable‑length outputs, and continuous pose estimation by formulating the task as set prediction and jointly training with a segmentation‑enhanced optimization module. The authors also introduce a large‑scale dataset of over 80,000 shapes and demonstrate that PART generalizes to scene layouts, image targets, and real‑world scans.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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