Localization-based active learning (LOCAL) for object detection in 3D point clouds

Abstract

Deep learning-based object detection and classification in 3D point clouds has numerous applications including defense, autonomous driving, and augmented reality. A challenge in applying deep learning to point clouds is the frequent scarcity of labeled data. Often, one must manually label a large quantity of data for the model to be useful in application. To overcome this challenge, active learning provides a means of minimizing the manual labeling required. The crux of active learning algorithms is defining and calculating the potential added “value” of labeling each unlabeled sample. We introduce a novel active learning algorithm, LOCAL, with an anchorbased object detection architecture, a modified object matching strategy, and an acquisition metric designed for object detection in any dimension. We compare the performance of common acquisition functions to our novel metric that utilizes all of the model outputs—including both bounding box localizations and softmax classification scores—to capture both the classification and spatial uncertainty in the model. Finally, we identify opportunities for further exploration, such as alternative measures of spatial uncertainty as well as increasing the stochasticity of the model in order to improve robustness of the algorithm.