Recently, Bird’s-Eye View (BEV) representation has received increasing attention in multi-view 3D object detection. However, training high-performance camera-based BEV 3D detectors typically requires cumbersome annotated training samples. In this challenging scenario, we are the pioneers in addressing the problem of semi-supervised BEV 3D object detection. To this end, we propose object-guided semi-supervised BEV 3D object detection (OSS3D), which trains the 3D detectors by using a small amount of labeled data and a large amount of unlabeled data to reduce the cost of data labeling. Firstly, our approach employs an object-guided Gaussian-like mask generation paired with multi-perspective geometric correspondence to emphasize foreground regions and mitigate background noise, ensuring accurate object alignment across different spaces. Additionally, this Gaussian-like mask is integral to our object-guided deep feature map supervision strategy. Secondly, in order to avoid the impact of unreliable pseudo-labels on student network training and improve the importance of object depth information in real space, we propose a self-training depth-based refinement algorithm to generate high-quality and stable pseudo-labels. Finally, our method can be easily inserted into a variety of BEV 3D detection networks. Several experiments demonstrate that the proposed method outperforms current semi-supervised approaches by 2.59% mAP and 2.94% NDS in the nuScenes dataset, achieving new state-of-the-art (SOTA) compared to various camera-based 3D detectors. Code will be available athttps://github.com/yangzhaojason/OSS3D

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.