  Achieving a reliable LiDAR-based object detector in autonomous driving is
paramount, but its success hinges on obtaining large amounts of precise 3D
annotations. Active learning (AL) seeks to mitigate the annotation burden
through algorithms that use fewer labels and can attain performance comparable
to fully supervised learning. Although AL has shown promise, current approaches
prioritize the selection of unlabeled point clouds with high uncertainty and/or
diversity, leading to the selection of more instances for labeling and reduced
computational efficiency. In this paper, we resort to a novel kernel coding
rate maximization (KECOR) strategy which aims to identify the most informative
point clouds to acquire labels through the lens of information theory. Greedy
search is applied to seek desired point clouds that can maximize the minimal
number of bits required to encode the latent features. To determine the
uniqueness and informativeness of the selected samples from the model
perspective, we construct a proxy network of the 3D detector head and compute
the outer product of Jacobians from all proxy layers to form the empirical
neural tangent kernel (NTK) matrix. To accommodate both one-stage (i.e.,
SECOND) and two-stage detectors (i.e., PVRCNN), we further incorporate the
classification entropy maximization and well trade-off between detection
performance and the total number of bounding boxes selected for annotation.
Extensive experiments conducted on two 3D benchmarks and a 2D detection dataset
evidence the superiority and versatility of the proposed approach. Our results
show that approximately 44% box-level annotation costs and 26% computational
time are reduced compared to the state-of-the-art AL method, without
compromising detection performance.
