PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion

• URL: http://arxiv.org/abs/2412.08421v2
• Date: Sat, 14 Dec 2024 15:14:24 GMT
• Title: PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion
• Authors: Yi Zhong, Weize Quan, Dong-ming Yan, Jie Jiang, Yingmei Wei,
• Abstract summary: Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds. We introduce PointCFormer, a transformer framework optimized for robust global retention and precise local detail capture. PointCFormer demonstrates state-of-the-art performance on several widely used benchmarks.
• Score: 19.503392612245474
• License:
• Abstract: Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we introduce PointCFormer, a transformer framework optimized for robust global retention and precise local detail capture in point cloud completion. This framework embraces several key advantages. First, we propose a relation-based local feature extraction method to perceive local delicate geometry characteristics. This approach establishes a fine-grained relationship metric between the target point and its k-nearest neighbors, quantifying each neighboring point's contribution to the target point's local features. Secondly, we introduce a progressive feature extractor that integrates our local feature perception method with self-attention. Starting with a denser sampling of points as input, it iteratively queries long-distance global dependencies and local neighborhood relationships. This extractor maintains enhanced global structure and refined local details, without generating substantial computational overhead. Additionally, we develop a correction module after generating point proxies in the latent space to reintroduce denser information from the input points, enhancing the representation capability of the point proxies. PointCFormer demonstrates state-of-the-art performance on several widely used benchmarks. Our code is available at https://github.com/Zyyyyy0926/PointCFormer_Plus_Pytorch.

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