用优化算法选关键点云数据，大幅降低3D目标检测标注成本
STONE: A Submodular Optimization Framework for Active 3D Object Detection
- 基于子模优化设计主动学习框架，自动筛选最具信息量的点云样本
- 在多个数据集上以更低标注量达到领先检测性能，效率显著优于现有方法
- 适合需要减少标注负担的自动驾驶与机器人研发团队使用
3D目标检测对自动驾驶和机器人等应用至关重要。训练高精度3D检测器需大量基于激光雷达的点云数据，但标注过程极为困难，需为每个潜在物体添加精确的3D边界框和语义标签。本文提出一种统一的主动3D目标检测框架，可显著降低训练所需标注成本。该框架基于针对主动3D检测问题定制的新型子模优化公式，有效解决数据不平衡及覆盖不同难度点云分布的两大挑战。大量实验表明，相比现有主动学习方法，本方法在保持高计算效率的同时实现最先进的性能。代码已公开于https://github.com/RuiyuM/STONE。
原文摘要 · Abstract (English)
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.
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