  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.
