Geometric Back-Projection Network for Point Cloud Classification

By Shi Qiu, Saeed Anwar, Nick Barnes. IEEE transactions on multimedia, 2019

295 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/48ea06e0b1f8cb6332fe0361de33ce77004cf640

Citations in the corpus

• Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework (2022, unknown): “(Xu et al., 2018) 69.8 73.7 PointNet++ (Qi et al., 2017b) 75.4 77.9 DGCNN (Wang et al., 2019) 73.6 78.1 PointCNN (Li et al., 2018b) 75.1 78.5 BGA-DGCNN (Uy et al., 2019) 75.7 79.7 BGA-PN++ (Uy et al., 2019) 77.5 80.2 DRNet (Qiu et al., 2021a) 78.0 80.3 GBNet (Qiu et al., 2021b) 77.8 80.5 SimpleView ”
• Reinforced Attention for Few-Shot Learning and Beyond (2021, unknown): “Those attention blocks either focus on the channel encoding [17, 39] or spatial context connection [56].”