HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely-Supervised 3D Object Detection
Authors: Qiming Xia, Wei Ye, Hai Wu, Shijia Zhao, Leyuan Xing, Xun Huang, Jinhao Deng, Xin Li, Chenglu Wen, Cheng Wang

Current sparsely-supervised object detection methods largely depend on high threshold settings to derive high-quality pseudo labels from detector predictions. However hard instances within point clouds frequently display incomplete structures causing decreased confidence scores in their assigned pseudo-labels. Previous methods inevitably result in inadequate positive supervision for these instances. To address this problem we propose a novel Hard INsTance Enhanced Detector HINTED for sparsely-supervised 3D object detection. Firstly we design a self-boosting teacher SBT model to generate more potential pseudo-labels enhancing the effectiveness of information transfer. Then we introduce a mixed-density student MDS model to concentrate on hard instances during the training phase thereby improving detection accuracy. Our extensive experiments on the KITTI dataset validate our method's superior performance. Compared with leading sparsely-supervised methods HINTED significantly improves the detection performance on hard instances notably outperforming fully-supervised methods in detecting challenging categories like cyclists. HINTED also significantly outperforms the state-of-the-art semi-supervised method on challenging categories. The code is available at https://github.com/xmuqimingxia/HINTED.