Improving 3D Vulnerable Road User Detection With Point Augmentation

doi:10.1109/TIV.2023.3246797

Improving 3D Vulnerable Road User Detection With Point Augmentation

2023-05-01

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DOI：10.1109/TIV.2023.3246797

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Point clouds have been a popular representation to describe 3D environments for autonomous driving applications. Despite accurate depth information, sparsity of points results in difficulties in extracting sufficient features from vulnerable objects of small sizes. One solution is leveraging self-attention networks to build long-range connections between similar objects. Another method is using generative models to estimate the complete shape of objects. Both approaches introduce large memory consumption and extra complexity to the models while the geometric characteristics of objects are overlooked. To overcome this problem, this paper proposes Point Augmentation (PA)-RCNN, focusing on small object detection by generating efficient complementary features without trainable parameters. Specifically, 3D points are sampled with the guidance of object proposals and encoded through the 3D grid-based feature aggregation to produce localised 3D voxel properties. Such voxel attributes are fed to the pooling module with the aid of fictional points, which are transformed from sampled points considering geometric symmetry. Experimental results on Waymo Open Dataset and KITTI dataset show a superior advantage in the detection of distant and small objects in comparison with existing state-of-the-art methods.

Keywords:

Three-dimensional displays

Feature extraction

Point cloud compression

Proposals

Shape

Transformers

Object detection

3D object detection

autonomous driving

intelligent vehicles

light detection and ranging (LiDAR) point clouds

Improving 3D Vulnerable Road User Detection With Point Augmentation

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