# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 6 Discussion  
Building a robust, accurate, and efficient 3D object detector is a critical mission to realize an intelligent perception system of an autonomous vehicle. The mainstream efforts utilize anchor-based methods to predict bounding boxes, which involve inefficient post-processing work, namely NMS, to enumerate a list of candidate object locations and classify each. To address this challenge, recent studies explore a point-based detector that uses key points to estimate the center point of an object and then regress other object properties. A representative work, CenterNet [20], has demonstrated superior performance in both detection accuracy and speed on the COCO dataset, compared to other anchor-based methods. The novelty and efficiency of CenterNet drive us to explore its application in autonomous driving. In this paper, we develop a center point-based DNN named RCBi-CenterNet for object detection in autonomous driving. The proposed RCBi-CenterNet predicts the center points using a heatmap of confidence scores and selects the peak values to represent detected objects. In addition, our method regresses the object depth and orientation, which describe  
Figure 10: Inference speed. Figure 9: Performance comparison. the absolute pose of an object, along with the position information given by the heatmap. RCBi-CenterNet is powered by a recursive composite network that consists of a dual-backbone module and a BiFPN module for cross-scale feature fusion. We conducted a series of experiments to validate the design choices of various data augmentation methods and backbones and selected HFlip and Se_ResNet101 as the best options for model integration. Finally, an overall comparison between RCBi-CenterNet and the SOTA, CenterNet, is reported. The results show the superiority of RCBi-CenterNet, with 2.16%, 2.76%, and 5.24% performance gains in Top 1, Top 3, and Top 10, respectively, demonstrating the efficacy of the proposed method. The pitch and roll degrees in the dataset do not carry valuable information since vehicle objects are captured on a flat road and are at the same horizontal level as the camera sensor. This is commonly seen in the current self-driving scenario. However, it should be noted that the usage of 6 DOF as a prediction target can be extended to the object detection task in more autonomous driving scenarios, such as unmanned aerial drones (UAV), underwater drones, or even in the outer space, where the absolute 3D position of detected objects, with more meaningful data in all six degrees, can be provided. This work has the following limitations, which also point out our future directions. First, the performance boost brought by RCBi-CenterNet comes with a cost, i.e., a drop of the detection speed; although the resulting speed, 13.2 FPS, can meet the real-time requirement, it can be further improved. The root cause of the performance drop is the added complexity of the feature extractor, which requires image features to go through multiple layers horizontally, vertically, and recursively. Thus, one direction is to simplify the DNN architecture, keeping the model slim and fast. Second, there are other feature extraction techniques to be tested in our system, such as CNN attention modules [59] and feature interaction [60]. Third, a generative data augmentation method can further enrich the vehicle samples in the original dataset. However, a challenge would be to automatically generate training samples with the 3D position annotations compatible with the existing system. Fourth, it would be interesting to explore the effect of other kernels than the Gaussian kernel. The kernel is a crucial design choice because it decides how the pixels close to the center point are marked as positive in the ground truth heatmap, which determines the sample distribution and directly impacts detection accuracy. Lastly, we plan to investigate a point-based camera-Lidar fusion model, which could offer a promising perspective to complement the current research efforts. Conceptualization and methodology, K.A., Y.C., S.W.