DDRNet: Depth Map Denoising and Refinement
for Consumer Depth Cameras Using
Cascaded CNNs
Abstract. Consumer depth sensors are more and more popular and
come to our daily lives marked by its recent integration in the latest
Iphone X. However, they still suffer from heavy noises which limit their
applications. Although plenty of progresses have been made to reduce the
noises and boost geometric details, due to the inherent illness and the
real-time requirement, the problem is still far from been solved. We propose a cascaded Depth Denoising and Refinement Network (DDRNet) to
tackle this problem by leveraging the multi-frame fused geometry and the
accompanying high quality color image through a joint training strategy.
The rendering equation is exploited in our network in an unsupervised
manner. In detail, we impose an unsupervised loss based on the light
transport to extract the high-frequency geometry. Experimental results
indicate that our network achieves real-time single depth enhancement on various categories of scenes. Thanks to the well decoupling of the
low and high frequency information in the cascaded network, we achieve
superior performance over the state-of-the-art techniques