  Dense prediction tasks are common for 3D point clouds, but the uncertainties
inherent 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 in 3D point clouds. Inspired by metric learning, the key idea
of CUE is to explore cross-point embeddings upon a conventional 3D dense
prediction pipeline. Specifically, CUE involves building a probabilistic
embedding model and then enforcing metric alignments of massive points in the
embedding space. We also propose CUE+, which enhances CUE by explicitly
modeling crosspoint dependencies in the covariance matrix. We demonstrate that
both CUE and CUE+ are generic and effective for uncertainty estimation in 3D
point clouds with two different tasks: (1) in 3D geometric feature learning we
for the first time obtain wellcalibrated uncertainty, and (2) in semantic
segmentation we reduce uncertainty's Expected Calibration Error of the
state-of-the-arts by 16.5%. All uncertainties are estimated without
compromising predictive performance.
