CSD3D: Cross-Scale Distillation via Dual-Consistency Learning for Semi-Supervised 3D Object Detection

Semi-supervised 3D object detection has gained significant attention due to its potential to mitigate the heavy reliance on extensive annotations in traditional 3D object detection methodologies. Most existing approaches leverage the teacher’s predictions to guide and refine the student’s predictions while discarding low-confidence predictions using a fixed threshold. However, the presence of imbalanced variance in object scale poses challenges as different objects often exhibit varying levels of detection difficulty. Current methods relying on pseudo labels struggle to comprehensively capture information pertaining to objects across diverse scales. To address these challenges, we propose CSD3D, a cross-scale distillation approach via dual-consistency learning. CSD3D encompasses cross-scale distillation between the teacher and student as well as within the student itself, thereby enhancing the algorithm’s resilience to scale variance. Moreover, by adopting a dual-consistency learning paradigm that incorporates supervision at both feature and prediction levels, our approach provides comprehensive guidance to the student model. This integration of dual-consistency learning within cross-scale conditions is conducive to comprehending cross-scale object features and maintaining scale-consistent predictions. Rigorous experiments performed on the ScanNet and SUN RGB-D benchmarks reveal that CSD3D attains state-of-the-art performance. By utilizing a mere 10% of labeled data on ScanNet, we observe absolute improvements of 3.8 and 3.5 in mAP@0.25 and mAP@0.5, respectively.