Towards Accurate Semi-Supervised BEV 3D Object Detection with Depth-Aware Refinement and Denoising-Aided Alignment

Recently, camera-based Bird's-Eye View (BEV) representation has gained significant traction in 3D object detection. However, training high-performance BEV 3D detectors typically requires a large number of annotated samples, which can be costly. Traditional semi-supervised methods for BEV 3D object detection face challenges including loss of rich depth information, inconsistent object representations across spaces, and unreliable pseudo label generation, leading to decreased accuracy and performance. Addressing this challenge, we pioneer the introduction of a semi-supervised BEV 3D object detection framework. Our approach leverages a small set of labeled data alongside a larger set of unlabeled data, significantly reducing annotation costs while maintaining robust detection performance. Firstly, we propose a depth-based self-refinement module to generate high-quality and stable pseudo labels, which can effectively regulate training with noisy labels. Secondly, we designed a denoising labels regression module that integrates denoising for both labeled and unlabeled data. Thirdly, in order to alleviate object inconsistency, we propose a consistent object-guided alignment method to ensure the consistency of objects in multi-spaces. Finally, our method can be easily plugged into various BEV 3D detection networks. Extensive experiments show that the proposed method achieves a new state-of-the-art compared to various camera-based 3D detectors tested on multiple public autonomous driving datasets.