LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency

Weilong Yan, Haipeng Li, Hao Xu, Nianjin Ye, Ai Yihao, Shuaicheng Liu, Jingyu Hu

LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency: 2 upvotes on Hugging Face Daily Papers, #29 of 32 papers on 2026-02-25. Day-by-day upvote history.

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}.

Paper page on Hugging Face · arXiv

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