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The task of detecting 3D objects in traffic scenes has a pivotal role in manyreal-world applications. However, the performance of 3D object detection islower than that of 2D object detection due to the lack of powerful 3D featureextraction methods. To address this issue, this study proposes a 3D backbonenetwork to acquire comprehensive 3D feature maps for 3D object detection. Itprimarily consists of sparse 3D convolutional neural network operations in thepoint cloud. The 3D backbone network can inherently learn 3D features from theraw data without compressing the point cloud into multiple 2D images. Thesparse 3D convolutional neural network takes full advantage of the sparsity inthe 3D point cloud to accelerate computation and save memory, which makes the3D backbone network feasible in a real-world application. Empirical experimentswere conducted on the KITTI benchmark and comparable results were obtained withrespect to the state-of-the-art performance for 3D object detection.

Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes