3D object detection is fundamentally important for various emerging applications, including autonomous driving and robotics. A key requirement for training an accurate 3D object detector is the availability of a large amount of LiDAR-based point cloud data. Unfortunately, labeling point cloud data is extremely challenging, as accurate 3D bounding boxes and semantic labels are required for each potential object. This paper proposes a unified active 3D object detection framework, for greatly reducing the labeling cost of training 3D object detectors. Our framework is based on a novel formulation of submodular optimization, specifically tailored to the problem of active 3D object detection. In particular, we address two fundamental challenges associated with active 3D object detection: data imbalance and the need to cover the distribution of the data, including LiDAR-based point cloud data of varying difficulty levels. Extensive experiments demonstrate that our method achieves state-of-the-art performance with high computational efficiency compared to existing active learning methods. The code is available at https://github.com/RuiyuM/STONE.

翻译：三维物体检测对于自动驾驶和机器人等新兴应用至关重要。训练精确三维物体检测器的关键前提是获取大量基于激光雷达的点云数据。然而，点云数据的标注极具挑战性，因为每个潜在物体都需要精确的三维边界框和语义标签。本文提出了一种统一的主动三维物体检测框架，旨在显著降低训练三维物体检测器的标注成本。该框架基于一种新颖的子模优化公式，专门针对主动三维物体检测问题设计。我们特别解决了与主动三维物体检测相关的两个基本挑战：数据不平衡性以及需要覆盖数据分布（包括不同难度级别的基于激光雷达的点云数据）。大量实验表明，与现有主动学习方法相比，我们的方法在保持高计算效率的同时实现了最先进的性能。代码发布于 https://github.com/RuiyuM/STONE。