# Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection Source: [Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:09:40.360Z Authors: Chuandong Liu, Chenqiang Gao, Fangcen Liu, Pengcheng Li, Deyu Meng, Xinbo Gao Published: 2023-04-04T02:09:32.000Z ## Abstract State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudolabeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSDA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://github.com/azhuantou/HSSDA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on KITTI 1% (val)](): 3D Object Detection; KITTI 1% (val); AP (Car): 81.7. - [3D Object Detection on KITTI (2% val)](): 3D Object Detection; KITTI (2% val); Car AP: 82. - [3D Object Detection on Waymo 1% label (val)](): 3D Object Detection; Waymo 1% label (val); Vehicle (L1) AP: 56.4. - [3D Object Detection on Waymo Open Dataset (val)](): 3D Object Detection; Waymo Open Dataset (val); 3D APH Vehicle L2: not recorded. ## Original sources and implementation [Original paper]() [PDF]()