HoloPart: Generative 3D Part Amodal Segmentation
Authors: name: Yunhan Yang, name: Yuan-Chen Guo, name: Yukun Huang, name: Zi-Xin Zou, name: Zhipeng Yu, name: Yangguang Li, name: Yan-Pei Cao, name: Xihui Liu

3D part amodal segmentation--decomposing a 3D shape into complete,
semantically meaningful parts, even when occluded--is a challenging but crucial
task for 3D content creation and understanding. Existing 3D part segmentation
methods only identify visible surface patches, limiting their utility. Inspired
by 2D amodal segmentation, we introduce this novel task to the 3D domain and
propose a practical, two-stage approach, addressing the key challenges of
inferring occluded 3D geometry, maintaining global shape consistency, and
handling diverse shapes with limited training data. First, we leverage existing
3D part segmentation to obtain initial, incomplete part segments. Second, we
introduce HoloPart, a novel diffusion-based model, to complete these segments
into full 3D parts. HoloPart utilizes a specialized architecture with local
attention to capture fine-grained part geometry and global shape context
attention to ensure overall shape consistency. We introduce new benchmarks
based on the ABO and PartObjaverse-Tiny datasets and demonstrate that HoloPart
significantly outperforms state-of-the-art shape completion methods. By
incorporating HoloPart with existing segmentation techniques, we achieve
promising results on 3D part amodal segmentation, opening new avenues for
applications in geometry editing, animation, and material assignment.