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