基于置信域伪标签策略的半监督三维目标检测.

Current point cloud-based 3D object detection methods largely rely on large-scale, high-quality 3D annotations. In order to reduce the required amount of labels, this paper proposed a new 3D object detection method based on the SESS network, trust region pseudo-supervised strategy for semi-supervised 3D object detection. Firstly, this paper designed a trust region pseudo-supervised strategy, which divided the output of the student network into labeled and unlabeled parts. The labeled part used ground truth for supervised learning, and based on the class and object prediction confidence score of the teacher network, the unlabeled part used an effective filtering mechanism to filter out high-quality teacher predictions and converted them into corresponding pseudo-labels for supervising the unlabeled part of the student network. Secondly, this paper designed a Trans Vote module, which used the Transformer mechanism to enhance the mutual attention between each point cloud and its neighboring points and aggregated the local features of the point cloud.With 10% labeled data and mAP@0.25,this method surpasses the baseline by 8.63% and 6.75% on the ScanNetV2 and SUN RGB-D datasets, which significantly improves the detection accuracy of semi-supervised 3D object detection algorithm. [ABSTRACT FROM AUTHOR]