3DSimDet: Simple yet Effective Semi-Supervised 3D Object Detector for Autonomous Driving

Abstract

For safe driving of autonomous vehicles, it is crucial to detect 3D objects within point clouds with both real-time performance and accuracy. Currently, many autonomous driving research groups adopt simple and efficient pillar-based 3D detectors for real-time performance. However, these detectors often apply grids of large size, which can potentially lead to the loss of significant point information. Moreover, these detectors commonly rely on post-processing steps such as NMS and pseudo-label generation. This reliance can adversely impact detection accuracy because it does not fully reflect the object's location information. To address these issues, we propose 3DSimDet, an efficient and compact 3D object detection framework suitable for autonomous driving platforms. This framework comprises two key components. The proposed SimBackbone module is designed to enhance the feature encoding capabilities of traditional methods. BIoU Head module includes a classification branch and an IoU prediction branch, which considers the object's location information during inference. Moreover, we introduce a high-quality pseudo-label generator for semi-supervised learning, leveraging the prediction outcomes from the BIoU Head module during the semi-supervised learning phase. Through extensive experimentation on diverse public autonomous driving datasets such as WOD, H3D, and A3D, we demonstrate the effectiveness of our proposed method. The experimental findings demonstrate that our 3DSimDet exhibits superior overall performance in terms of accuracy and runtime compared to conventional pillar-based supervised learning models. Furthermore, the pseudo-label generator proves to enhance detection performance within a semi-supervised learning framework. © 2024 IEEE.