GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
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The inherent ambiguity in ground-truth annotations of 3D bounding boxes caused by occlusions, signal missing, or manual annotation errors can confuse deep 3D object detectors during training, thus deteriorating the detection accuracy. However, existing methods overlook such issues to some extent and treat the labels as deterministic. In this paper, we propose GLENet, a generative label uncertainty estimation framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and observe that their performance is significantly boosted to the current state-of-the-art over the Waymo Open dataset and KITTI dataset.
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