  3D object detection has recently received much attention due to its great
potential in autonomous vehicle (AV). The success of deep learning based object
detectors relies on the availability of large-scale annotated datasets, which
is time-consuming and expensive to compile, especially for 3D bounding box
annotation. In this work, we investigate diversity-based active learning (AL)
as a potential solution to alleviate the annotation burden. Given limited
annotation budget, only the most informative frames and objects are
automatically selected for human to annotate. Technically, we take the
advantage of the multimodal information provided in an AV dataset, and propose
a novel acquisition function that enforces spatial and temporal diversity in
the selected samples. We benchmark the proposed method against other AL
strategies under realistic annotation cost measurement, where the realistic
costs for annotating a frame and a 3D bounding box are both taken into
consideration. We demonstrate the effectiveness of the proposed method on the
nuScenes dataset and show that it outperforms existing AL strategies
significantly.
