LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across diverse types of partial observations. Our contributions are threefold: First,  harnesses these powerful generative priors for completion through a complementary two-stage design: (i) an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion; and (ii) an implicit refinement stage ensures seamless boundaries between the observed and synthesized regions. Second, our framework is training-free and compatible with different 3D foundation models. Third, we introduce Omni-Comp, a comprehensive benchmark combining real-world and synthetic data with diverse and challenging partial patterns, enabling a more thorough and realistic evaluation. Both quantitative and qualitative experiments demonstrate that our approach outperforms previous state-of-the-art approaches. Our code and data will be available at https://github.com/DavidYan2001/LaS-Comp{LaS-Comp}.
699d85ab9491ce7a9c6889ef
Weilong Yan
claimed_verified
2026-02-25T17:31:13.336Z
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Weilong Yan
DavidYan2001
user
DavidYan2001
699d85ab9491ce7a9c6889f0
Haipeng Li
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Hao Xu
699d85ab9491ce7a9c6889f2
Nianjin Ye
699d85ab9491ce7a9c6889f3
Yihao Ai
claimed_verified
2026-03-03T10:43:49.856Z
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Ai Yihao
ayh015
user
ayh015
699d85ab9491ce7a9c6889f4
Shuaicheng Liu
699d85ab9491ce7a9c6889f5
Jingyu Hu
LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across diverse types of partial observations. Our contributions are threefold: First,  harnesses these powerful generative priors for completion through a complementary two-stage design: (i) an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion; and (ii) an implicit refinement stage ensures seamless boundaries between the observed and synthesized regions. Second, our framework is training-free and compatible with different 3D foundation models. Third, we introduce Omni-Comp, a comprehensive benchmark combining real-world and synthetic data with diverse and challenging partial patterns, enabling a more thorough and realistic evaluation. Both quantitative and qualitative experiments demonstrate that our approach outperforms previous state-of-the-art approaches. Our code and data will be available at https://github.com/DavidYan2001/LaS-Comp{LaS-Comp}.
LaS-Comp presents a zero-shot 3D shape completion method using 3D foundation models with a two-stage approach for faithful reconstruction and seamless boundary refinement.
3D shape completion 3D foundation models geometric priors zero-shot category-agnostic two-stage design explicit replacement stage implicit refinement stage training-free Omni-Comp comprehensive benchmark