Geometric Back-projection Network for Point Cloud Classification
This work addresses a fundamental challenge in point cloud analysis for applications like 3D vision, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles point cloud classification by proposing a network that captures geometric features through low-level explicit enrichment and high-level implicit learning, achieving superior performance on synthetic and real-world datasets with balanced accuracy and efficiency.
As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.