PointeNet

PointeNet: A lightweight framework for effective and efficient point cloud analysis

Gu, Lipeng (Nanjing University of Aeronautics and Astronautics)
Yan, Xuefeng (Nanjing University of Aeronautics and Astronautics; Nanjing University)
Nan, L. (TU Delft Urban Data Science)
Zhu, Dingkun (Jiangsu University of Technology)
Chen, Honghua (Nanjing University of Aeronautics and Astronautics)
Wang, Weiming (Hong Kong Metropolitan University)
Wei, Mingqiang (Nanjing University of Aeronautics and Astronautics)

2024

The conventional wisdom in point cloud analysis predominantly explores 3D geometries. It is often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these methods contain a significant number of learnable parameters, resulting in substantial computational costs and imposing memory burdens on CPU/GPU. Moreover, they are primarily tailored for object-level point cloud classification and segmentation tasks, with limited extensions to crucial scene-level applications, such as autonomous driving. To this end, we introduce PointeNet, an efficient network designed specifically for point cloud analysis. PointeNet distinguishes itself with its lightweight architecture, low training cost, and plug-and-play capability, while also effectively capturing representative features. The network consists of a Multivariate Geometric Encoding (MGE) module and an optional Distance-aware Semantic Enhancement (DSE) module. MGE employs operations of sampling, grouping, pooling, and multivariate geometric aggregation to lightweightly capture and adaptively aggregate multivariate geometric features, providing a comprehensive depiction of 3D geometries. DSE, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks, achieving notable performance improvements at a minimal cost. Extensive experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis. Notably, PointeNet outperforms PointMLP with significantly fewer parameters on ModelNet40, ScanObjectNN, and ShapeNetPart, and achieves a substantial improvement of over 2% in 3DAP_R40 for PointRCNN on KITTI with a minimal parameter cost of 1.4 million. Code is publicly available at https://github.com/lipeng-gu/PointeNet

3D object detection
Distance-aware semantic enhancement
Multivariate geometric encoding
Point cloud analysis
PointeNet

http://resolver.tudelft.nl/uuid:8920d27b-255e-4ab4-bd67-1e2b24ff36cf

https://doi.org/10.1016/j.cagd.2024.102311

2024-10-24

0167-8396

Computer-Aided Geometric Design, 110

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Lipeng Gu, Xuefeng Yan, L. Nan, Dingkun Zhu, Honghua Chen, Weiming Wang, Mingqiang Wei