@inproceedings{
yu2024geoformer,
title={GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer},
author={Jinpeng Yu and Binbin Huang and Yuxuan Zhang and Huaxia Li and Xu Tang and Shenghua Gao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=65p0B1QQi9}
}
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. The code is ready and will be released soon.
GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer
Jinpeng Yu Binbin Huang Yuxuan Zhang Huaxia Li Xu Tang Shenghua Gao