High performance point-Voxel feature set abstraction with mamba for 3D object detection.
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| Title: | High performance point-Voxel feature set abstraction with mamba for 3D object detection. |
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| Authors: | Ren, Junfeng1 (AUTHOR), Wen, Changji1,2 (AUTHOR) changjiw@jlau.edu.cn, Zhang, Long1 (AUTHOR), Su, Hengqiang1 (AUTHOR), Yang, Ce1,3 (AUTHOR), Lv, Yanfeng4 (AUTHOR), Yang, Ning2 (AUTHOR), Qin, Xiwen1,5 (AUTHOR) qinxiwen@ccut.edu.cn |
| Source: | Expert Systems with Applications. Aug2025, Vol. 286, pN.PAG-N.PAG. 1p. |
| Subjects: | Object recognition (Computer vision), Convolutional neural networks, Feature extraction, Point cloud, LIDAR |
| Abstract: | • HP-PV-RCNN for 3D object detection on LiDAR point clouds. • Backbone based on mamba2 and linear angle attention used to extract 3D features. • The pointhead based on kolmogorov-arnold networks for extracting features of points. • Fuzzy NMS with an adaptive threshold removes redundant detection boxes. • HP-PV-RCNN has achieved good results on the Kitti, NuScenes, and Waymo open datasets. In the field of autonomous driving, a two-stage three-dimensional object detection approach has seen significant advancements. However, challenges persist in terms of detection accuracy, which can have a profound impact on the safety of autonomous vehicles. This study examined four critical issues that impair the accuracy and efficiency of the model: limited acceptance fields, slow acquisition of global features from voxels, challenges in capturing keypoint features, and uncertainties associated with network post-processing. To address these challenges, we propose four novel techniques: (1) a non-empty voxel feature extraction method that utilises linear angular attention to broaden the receptive field; (2) an efficient voxel feature extraction and downsampling approach based on Mamba2, designed to accelerate the acquisition of global voxel features; (3) a node extraction strategy that employs the Kolmogorov-Arnold Network (KAN) to extract key point features via segmented farthest point sampling (S-FPS); (4) a fuzzy non-maximum suppression (Fuzzy-NMS) method that refines suppression thresholds during the post-processing phase. By integrating these techniques, we introduce a High-Performance Point-Voxel Region Convolutional Neural Network (HP-PV-RCNN) algorithm specifically tailored for precise 3D object detection. We validated the effectiveness of the HP-PV-RCNN algorithm through comprehensive experiments using the Kitti, NuScenes, and Waymo open datasets. Specifically, our proposed network attained average precisions of 83.73 % for vehicles, 76.32 % for bicycles, and 53.52 % for pedestrians in the medium-difficulty category of the Kitti dataset for detecting these entities. The code and model are available at https://github.com/jlauwcj/HP-PV-RCNN. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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