  Depth sensing is crucial for 3D reconstruction and scene understanding.
Active depth sensors provide dense metric measurements, but often suffer from
limitations such as restricted operating ranges, low spatial resolution, sensor
interference, and high power consumption. In this paper, we propose a deep
learning (DL) method to estimate per-pixel depth and its uncertainty
continuously from a monocular video stream, with the goal of effectively
turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we
estimate a depth probability distribution for each pixel rather than a single
depth value, leading to an estimate of a 3D depth probability volume for each
input frame. These depth probability volumes are accumulated over time under a
Bayesian filtering framework as more incoming frames are processed
sequentially, which effectively reduces depth uncertainty and improves
accuracy, robustness, and temporal stability. Compared to prior work, the
proposed approach achieves more accurate and stable results, and generalizes
better to new datasets. Experimental results also show the output of our
approach can be directly fed into classical RGB-D based 3D scanning methods for
3D scene reconstruction.
