Abstract
With the advancement of automobile industry, autonomous driving has become an integral component of contemporary vehicles. In this context, the 3D spatial perception of vehicles plays a crucial role. The complexity of 3D object detection dramatically increased due to the explosion of degrees-of-freedom for 3D bounding boxes. Therefore, current 3D detection algorithms struggle to achieve high accuracy and efficiency simultaneously. To address this problem, we introduce a single-stage, LiDAR-based, and efficiency–accuracy balanced 3D object detector named 6DoF-3D. The proposed network architecture comprises a backbone of CSPDarknet53, a multi-scale feature fusion neck, and a detection head. We transform 3D point clouds into pseudo-images, with critical spatial features retained. 6DoF-3D presents a novel encoding strategy to prune the redundant degree-of-freedom in 3D detection, resulting in a lightweight network architecture. We put forward a 2.5D Euler-Region-Proposal network for precise classification and regression of 2.5D bounding boxes. The pruned dimension is compensated with a geometric transformation decoder. Extensive experiments conducted on the KITTI dataset demonstrate that 6DoF-3D achieves state-of-the-art accuracy of 89.62% for 3D car detection at the moderate level. Additionally, the mean average precision reaches 66.72% at the while maintaining a real-time performance of 33.21 FPS for cars, pedestrians, and cyclists.
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