Exploring Cross-Point Embeddings for 3D Dense Uncertainty Estimation
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Dense prediction tasks are common for 3D point clouds, but the inherent uncertainties in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks of 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We demonstrate that CUE is a generic and effective tool for dense uncertainty estimation of 3D point clouds in two different tasks: (1) in 3D geometric feature learning we for the first time obtain well-calibrated dense uncertainty, and (2) in semantic segmentation we reduce uncertainty`s Expected Calibration Error of the state-of-the-arts by 43.8 are estimated without compromising predictive performance.
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