SELECT selects annotation voxels using feature-variance ranking, Monte Carlo dropout uncertainty, and a class-balance entropy criterion, and reports mIoU gains over prior active learning baselines on three LiDAR benchmarks.
STONE: A Submodular Optimization Framework for Active 3D Object Detection
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abstract
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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SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation
SELECT selects annotation voxels using feature-variance ranking, Monte Carlo dropout uncertainty, and a class-balance entropy criterion, and reports mIoU gains over prior active learning baselines on three LiDAR benchmarks.