Document Type

Article

Publication Title

IEEE Access

Abstract

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection accuracy is severely restricted by the information loss of pose details in the voxels. Different from considering the point cloud as voxel or point representation only, we propose a point-to-voxel feature learning approach to voxelize the point cloud with both the point-wise semantic and local spatial features, which maintains the voxel-wise features to build the high-recall voxel-based RPN and also provides the accurate point-wise features for refining the detection results. Another difficulty in object detection for point cloud is that the visible part varies a lot against the full view of object because of the perspective issues in data acquisition. To address this, we propose an attentive corner aggregation module to attentively aggregate the features of local point cloud surrounding a 3D proposal from the perspectives of eight corners in the proposal 3D bounding box. The experimental results on the competitive KITTI 3D object detection benchmark show that the proposed method achieves state-of-the-art performance.

First Page

98249

Last Page

98260

DOI

10.1109/ACCESS.2021.3094562

Publication Date

7-5-2021

Keywords

3D object detection, attention mechanism, autonomous driving, point clouds

Comments

Open Access version with thanks to IEEE and IEEE Access

License: CC BY NC-ND 4.0

Uploaded 30 March 2022

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