  The basic framework of depth completion is to predict a pixel-wise dense
depth map using very sparse input data. In this paper, we try to solve this
problem in a more effective way, by reformulating the regression-based depth
estimation problem into a combination of depth plane classification and
residual regression. Our proposed approach is to initially densify sparse depth
information by figuring out which plane a pixel should lie among a number of
discretized depth planes, and then calculate the final depth value by
predicting the distance from the specified plane. This will help the network to
lessen the burden of directly regressing the absolute depth information from
none, and to effectively obtain more accurate depth prediction result with less
computation power and inference time. To do so, we firstly introduce a novel
way of interpreting depth information with the closest depth plane label $p$
and a residual value $r$, as we call it, Plane-Residual (PR) representation. We
also propose a depth completion network utilizing PR representation consisting
of a shared encoder and two decoders, where one classifies the pixel's depth
plane label, while the other one regresses the normalized distance from the
classified depth plane. By interpreting depth information in PR representation
and using our corresponding depth completion network, we were able to acquire
improved depth completion performance with faster computation, compared to
previous approaches.
