# Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection Source: [Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:06.167Z Authors: Farzad Nozarian, Shashank Agarwal, Farzaneh Rezaeianaran, Danish Shahzad, Atanas Poibrenski, Christian M\\"uller, Philipp Slusallek Published: 2024-04-27T13:38:45.000Z ## Abstract Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited. However, recent approaches have overlooked the impact of noisy pseudo-labels during training, despite efforts to enhance pseudo-label quality through confidence-based filtering. In this paper, we examine the impact of noisy pseudo-labels on IoU-based target assignment and propose the Reliable Student framework, which incorporates two complementary approaches to mitigate errors. First, it involves a class-aware target assignment strategy that reduces false negative assignments in difficult classes. Second, it includes a reliability weighting strategy that suppresses false positive assignment errors while also addressing remaining false negatives from the first step. The reliability weights are determined by querying the teacher network for confidence scores of the student-generated proposals. Our work surpasses the previous state-of-the-art on KITTI 3D object detection benchmark on point clouds in the semi-supervised setting. On 1% labeled data, our approach achieves a 6.2% AP improvement for the pedestrian class, despite having only 37 labeled samples available. The improvements become significant for the 2% setting, achieving 6.0% AP and 5.7% AP improvements for the pedestrian and cyclist classes, respectively. ## 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 (2% val)](): 3D Object Detection; KITTI (2% val); Car AP: 79.5. - [3D Object Detection on KITTI 1% (val)](): 3D Object Detection; KITTI 1% (val); AP (Car): 77. ## Original sources and implementation [Original paper]() [PDF]()