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.

通过使用 Laplace 分布的损失函数，预测置信区间，设计了一种轻量级前馈神经网络，可以从 2D 人体姿势中预测出 3D 位置和对应的置信区间。该方法不仅在 KITTI 和 nuScenes 数据集上超过了最先进的结果，而且在远处行人的情况下，甚至超过了基于立体视觉的方法，并且可以估算有意义的置信区间。