Zero-shot Point Cloud Completion Via 2D Priors
<|reference_start|>Zero-shot Point Cloud Completion Via 2D Priors: 3D point cloud completion is designed to recover complete shapes from partially observed point clouds. Conventional completion methods typically depend on extensive point cloud data for training %, with their effectiveness often constrained to object categories similar to those seen during training. In contrast, we propose a zero-shot framework aimed at completing partially observed point clouds across any unseen categories. Leveraging point rendering via Gaussian Splatting, we develop techniques of Point Cloud Colorization and Zero-shot Fractal Completion that utilize 2D priors from pre-trained diffusion models to infer missing regions. Experimental results on both synthetic and real-world scanned point clouds demonstrate that our approach outperforms existing methods in completing a variety of objects without any requirement for specific training data.<|reference_end|>
@article{huang2024zero-shot,
    title={Zero-shot Point Cloud Completion Via 2D Priors},
    author={Tianxin Huang, Zhiwen Yan, Yuyang Zhao, Gim Hee Lee},
    journal={arXiv preprint arXiv:2404.06814},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06814},
    primaryClass={cs.CV}
}