# Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detection Source: [Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2026-04-07T05:00:04.607Z Authors: Taehun Kong, Tae-Kyun Kim Published: 2025-09-28T13:40:48.000Z ## Abstract Semi-supervised 3D object detection (SS3DOD) aims to reduce costly 3D annotations utilizing unlabeled data. Recent studies adopt pseudo-label-based teacher-student frameworks and demonstrate impressive performance. The main challenge of these frameworks is in selecting high-quality pseudo-labels from the teacher's predictions. Most previous methods, however, select pseudo-labels by comparing confidence scores over thresholds manually set. The latest works tackle the challenge either by dynamic thresholding or refining the quality of pseudo-labels. Such methods still overlook contextual information e.g. object distances, classes, and learning states, and inadequately assess the pseudo-label quality using partial information available from the networks. In this work, we propose a novel SS3DOD framework featuring a learnable pseudo-labeling module designed to automatically and adaptively select high-quality pseudo-labels. Our approach introduces two networks at the teacher output level. These networks reliably assess the quality of pseudo-labels by the score fusion and determine context-adaptive thresholds, which are supervised by the alignment of pseudo-labels over GT bounding boxes. Additionally, we introduce a soft supervision strategy that can learn robustly under pseudo-label noises. This helps the student network prioritize cleaner labels over noisy ones in semi-supervised learning. Extensive experiments on the KITTI and Waymo datasets demonstrate the effectiveness of our method. The proposed method selects high-precision pseudo-labels while maintaining a wider coverage of contexts and a higher recall rate, significantly improving relevant SS3DOD methods. ## 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): 81.4. - [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: 58.8. ## Original sources and implementation [Original paper]() [PDF]()