Point-attention Net: a graph attention convolution network for point cloudsegmentation

Chen, Suting; Miao, Zelin; Chen, Huaixin; Mukherjee, Mithun; Zhang, Yanyan

doi:10.1007/s10489-022-03985-4

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…[26] proposed two self-attention variants, FSA and DSA, for contextual modeling in 3D object detection, incorporating convolutional and self-attentive features in the model. Moreover, the attention mechanism can be used for point cloud segmentation, as seen in [36], where the network structure is based on the point-attention mechanism, effectively improving point cloud segmentation performance.…”

Section: Attention Mechanisms In Object Detectionmentioning

confidence: 99%

A Two-Stage Pillar Feature-Encoding Network for Pillar-Based 3D Object Detection

Xiang

et al. 2023

WEVJ

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Three-dimensional object detection plays a vital role in the field of environment perception in autonomous driving, and its results are crucial for the subsequent processes. Pillar-based 3D object detection is a method to detect objects in 3D by dividing point cloud data into pillars and extracting features from each pillar. However, the current pillar-based 3D object-detection methods suffer from problems such as “under-segmentation” and false detections in overlapping and occluded scenes. To address these challenges, we propose an improved pillar-based 3D object-detection network with a two-stage pillar feature-encoding (Ts-PFE) module that considers both inter- and intra-relational features among and in the pillars. This novel approach enhances the model’s ability to identify the local structure and global distribution of the data, which improves the distinction between objects in occluded and overlapping scenes and ultimately reduces under-segmentation and false detection problems. Furthermore, we use the attention mechanism to improve the backbone and make it focus on important features. The proposed approach is evaluated on the KITTI dataset. The experimental results show that the detection accuracy of the proposed approach are significantly improved on the benchmarks of BEV and 3D. The improvement of AP for car, pedestrian, and cyclist 3D detection are 1.1%, 3.78%, and 2.23% over PointPillars.

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Section: Attention Mechanisms In Object Detectionmentioning

confidence: 99%