LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
This addresses the challenge of completing 3D shapes across diverse partial patterns for applications in computer vision and graphics, representing a novel method rather than an incremental improvement.
The paper tackles the problem of 3D shape completion from partial observations by introducing LaS-Comp, a zero-shot and category-agnostic approach that leverages 3D foundation models, resulting in outperforming previous state-of-the-art methods in both quantitative and qualitative experiments.
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, \ourname{} 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 \href{https://github.com/DavidYan2001/LaS-Comp}{LaS-Comp}.