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.
Self-Supervised Pretraining of 3D Features on any Point-Cloud
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abstract
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc. However, pretraining is not widely used for 3D recognition tasks where state-of-the-art methods train models from scratch. A primary reason is the lack of large annotated datasets because 3D data is both difficult to acquire and time consuming to label. We present a simple self-supervised pertaining method that can work with any 3D data - single or multiview, indoor or outdoor, acquired by varied sensors, without 3D registration. We pretrain standard point cloud and voxel based model architectures, and show that joint pretraining further improves performance. We evaluate our models on 9 benchmarks for object detection, semantic segmentation, and object classification, where they achieve state-of-the-art results and can outperform supervised pretraining. We set a new state-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5% mAP). Our pretrained models are label efficient and improve performance for classes with few examples.
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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.