Inferring Distributions Over Depth from a Single Image When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as depth regression, whose output is a depth value for each pixel. Such approaches cannot diagnose when failures might occur. One attractive alternative is a deep Bayesian network, which captures uncertainty in both model parameters and ambiguous sensor measurements. However, estimating uncertainties is often slow and the distributions are often limited to be unimodal. In this paper, we recast the continuous problem of depth regression as discrete binary classification, whose output is an unnormalized distribution over possible depths for each pixel. Such output allows one to reliably and efficiently capture multimodal depth distributions in ambiguous cases, such as depth discontinuities and reflective surfaces. Results on standard benchmarks show that our method produces accurate depth predictions and significantly better uncertainty estimations than prior art while running near realtime. Finally, by making use of uncertainties of the predicted distribution, we significantly reduce streaklike artifacts and improves accuracy as well as memory efficiency in 3D map reconstruction. Video and code can be found on the project website1.

Hierarchical Binary Classification for Monocular Depth Estimation Extracting dense depth from a single image is an important and challenging task in computer vision. Recently, significant progress has been made in monocular depth estimation (MDE) by formulating this problem as a multilabel classification instead of traditional regression. However, to discretize depth into hundreds of labels results in complicate network output and a large number of parameters. In this paper, we propose to encode the discretized depth into a binary code and formulate MDE as a hierarchical binary classification (HBC) problem to reduce the complexity. Besides, by studying the distribution of individual bit of the encoded depth codes, we find that our encoding scheme can also solve the problem of depth data imbalance. We conduct experiments on KITTI and NYU Depth V2 datasets, which show that our simplified approach still achieves a comparable performance with stateoftheart methods.