Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detection

• URL: http://arxiv.org/abs/2509.23880v1
• Date: Sun, 28 Sep 2025 13:40:48 GMT
• Title: Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detection
• Authors: Taehun Kong, Tae-Kyun Kim,
• Abstract summary: Semi-supervised 3D object detection (SS3DOD) aims to reduce costly 3D datasets utilizing unlabeled data.<n>Recent studies adopt pseudo-label-based teacher-student frameworks and demonstrate impressive performance.<n>Main challenge of these frameworks is in selecting high-quality pseudo-labels from the teacher's predictions.<n>We propose a novel SS3DOD framework featuring a learnable pseudo-labeling module designed to automatically and adaptively select high-quality pseudo-labels.
• Score: 12.973657570368317
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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