Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes
This addresses the need for efficient 3D object detection in autonomous driving applications, though it appears incremental as it builds on existing sparse 3D CNN techniques.
The paper tackles the problem of 3D object detection in traffic scenes by proposing a 3D backbone network that learns features directly from point clouds without compression, achieving comparable results to state-of-the-art methods on the KITTI benchmark.
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.