3D Object detection is dragon ball lg disney a critical mission of the perception system of a self-driving vehicle.Existing bounding box-based methods are hard to train due to the need to remove duplicated detections in the post-processing stage.In this paper, we propose a center point-based deep neural network (DNN) architecture named RCBi-CenterNet that predicts the absolute pose for each detected object in the 3D world space.
RCBi-CenterNet is composed of a recursive composite network with a dual-backbone feature extractor and a bi-directional feature pyramid network (BiFPN) for cross-scale feature fusion.In the detection head, we predict a confidence heatmap that is used to determine the position of detected objects.The other pose information, including depth and orientation, is regressed.
We conducted extensive experiments on the Peking University/Baidu-Autonomous Driving dataset, which contains more than 60,000 labeled 3D vehicle instances from 5277 real-world images, and each vehicle object is annotated with the absolute pose described by the six turbo air m3f24-1-n degrees of freedom (6DOF).We validated the design choices of various data augmentation methods and the backbone options.Through an ablation study and an overall comparison with the state-of-the-art (SOTA), namely CenterNet, we showed that the proposed RCBi-CenterNet presents performance gains of 2.
16%, 2.76%, and 5.24% in Top 1, Top 3, and Top 10 mean average precision (mAP).
The model and the result could serve as a credible benchmark for future research in center point-based object detection.