  We tackle the fundamentally ill-posed problem of 3D human localization from
monocular RGB images. Driven by the limitation of neural networks outputting
point estimates, we address the ambiguity in the task by predicting confidence
intervals through a loss function based on the Laplace distribution. Our
architecture is a light-weight feed-forward neural network that predicts 3D
locations and corresponding confidence intervals given 2D human poses. The
design is particularly well suited for small training data, cross-dataset
generalization, and real-time applications. Our experiments show that we (i)
outperform state-of-the-art results on KITTI and nuScenes datasets, (ii) even
outperform a stereo-based method for far-away pedestrians, and (iii) estimate
meaningful confidence intervals. We further share insights on our model of
uncertainty in cases of limited observations and out-of-distribution samples.
