title: HoloPart: Generative 3D Part Amodal Segmentation
summary: 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.
paper_authors._id: 67f88643f5889583ce0499ae
paper_authors.name: Yunhan Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:28:45.498Z
paper_authors._id: 64b4eecf2fc8324fcb63b404
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b4eecf2fc8324fcb63b404/zGYqYVB4-o-GBMybJ8CDA.png
paper_authors.fullname: Yunhan Yang
paper_authors.type: user
paper_authors.user: yhyang-myron
paper_authors._id: 67f88643f5889583ce0499af
paper_authors.name: Yuan-Chen Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:28:51.310Z
paper_authors._id: 6346aaa3f06b237ba4e297b0
paper_authors.avatarUrl: /avatars/5acb986e993eab1461200f3e9d99d022.svg
paper_authors.fullname: Yuan-Chen Guo
paper_authors.type: user
paper_authors.user: bennyguo
paper_authors._id: 67f88643f5889583ce0499b0
paper_authors.name: Yukun Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-11T07:25:25.048Z
paper_authors._id: 638ee900ee7e45e0474a5712
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/638ee900ee7e45e0474a5712/KLli_eCbWwffKR7oLDmV3.jpeg
paper_authors.fullname: Yukun Huang
paper_authors.type: user
paper_authors.user: KevinHuang
paper_authors._id: 67f88643f5889583ce0499b1
paper_authors.name: Zi-Xin Zou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:29:00.746Z
paper_authors._id: 644dbf6453ad80c6593bf748
paper_authors.avatarUrl: /avatars/0e170cf2aa8d7f0f3f83e36f06f023f8.svg
paper_authors.fullname: Zixin Zou
paper_authors.type: user
paper_authors.user: zouzx
paper_authors._id: 67f88643f5889583ce0499b2
paper_authors.name: Zhipeng Yu
paper_authors._id: 67f88643f5889583ce0499b3
paper_authors.name: Yangguang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:29:23.548Z
paper_authors._id: 64d71083a787c9bc7b9f1238
paper_authors.avatarUrl: /avatars/d0b0546dec7fc5792921154bec41385a.svg
paper_authors.fullname: Yangguang Li
paper_authors.type: user
paper_authors.user: Lp256
paper_authors._id: 67f88643f5889583ce0499b4
paper_authors.name: Yan-Pei Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:29:32.598Z
paper_authors._id: 638066faf022c8a5803f7eb8
paper_authors.avatarUrl: /avatars/4cfd699c3f6c5461b12b7dc5e3fe183d.svg
paper_authors.fullname: Yanpei Cao
paper_authors.type: user
paper_authors.user: pookiefoof
paper_authors._id: 67f88643f5889583ce0499b5
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:29:39.026Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: HoloPart: Generative 3D Part Amodal Segmentation
paper_summary: 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.
paper_ai_summary: HoloPart, a diffusion-based model with local and global attention, addresses 3D part amodal segmentation by completing occluded parts and improving global shape consistency.
paper_ai_keywords: diffusion-based model local attention global shape context attention