ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
This work addresses the problem of reducing labeling costs for researchers and practitioners in autonomous driving and robotics, though it is incremental as it applies existing active learning methods to 3D object detection.
The paper tackles the high cost of labeling large-scale datasets for multi-modal 3D object detection by proposing ActiveAnno3D, an active learning framework that selects informative samples for labeling, achieving nearly the same performance with half the training data (e.g., 77.25 mAP vs. 83.50 mAP on TUM Traffic Intersection dataset).
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remains. In this work, we fill the research gap using active learning for multi-modal 3D object detection. We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training. We explore various continuous training methods and integrate the most efficient method regarding computational demand and detection performance. Furthermore, we perform extensive experiments and ablation studies with BEVFusion and PV-RCNN on the nuScenes and TUM Traffic Intersection dataset. We show that we can achieve almost the same performance with PV-RCNN and the entropy-based query strategy when using only half of the training data (77.25 mAP compared to 83.50 mAP) of the TUM Traffic Intersection dataset. BEVFusion achieved an mAP of 64.31 when using half of the training data and 75.0 mAP when using the complete nuScenes dataset. We integrate our active learning framework into the proAnno labeling tool to enable AI-assisted data selection and labeling and minimize the labeling costs. Finally, we provide code, weights, and visualization results on our website: https://active3d-framework.github.io/active3d-framework.