GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer
<|reference_start|>GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer: Point cloud completion aims to recover accurate global geometry and preserve fine-grained local details from partial point clouds. Conventional methods typically predict unseen points directly from 3D point cloud coordinates or use self-projected multi-view depth maps to ease this task. However, these gray-scale depth maps cannot reach multi-view consistency, consequently restricting the performance. In this paper, we introduce a GeoFormer that simultaneously enhances the global geometric structure of the points and improves the local details. Specifically, we design a CCM Feature Enhanced Point Generator to integrate image features from multi-view consistent canonical coordinate maps (CCMs) and align them with pure point features, thereby enhancing the global geometry feature. Additionally, we employ the Multi-scale Geometry-aware Upsampler module to progressively enhance local details. This is achieved through cross attention between the multi-scale features extracted from the partial input and the features derived from previously estimated points. Extensive experiments on the PCN, ShapeNet-55/34, and KITTI benchmarks demonstrate that our GeoFormer outperforms recent methods, achieving the state-of-the-art performance. Our code is available at \href{https://github.com/Jinpeng-Yu/GeoFormer}{https://github.com/Jinpeng-Yu/GeoFormer}.<|reference_end|>
@article{yu2024geoformer:,
    title={GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated
  Transformer},
    author={Jinpeng Yu, Binbin Huang, Yuxuan Zhang, Huaxia Li, Xu Tang, Shenghua
  Gao},
    journal={arXiv preprint arXiv:2408.06596},
    year={2024},
    doi={10.1145/3664647.3680842},
    archivePrefix={arXiv},
    eprint={2408.06596},
    primaryClass={cs.CV}
}