  Depth completion aims to derive a dense depth map from sparse depth
measurements with a synchronized color image. Current state-of-the-art (SOTA)
methods are predominantly propagation-based, which work as an iterative
refinement on the initial estimated dense depth. However, the initial depth
estimations mostly result from direct applications of convolutional layers on
the sparse depth map. In this paper, we present a Bilateral Propagation Network
(BP-Net), that propagates depth at the earliest stage to avoid directly
convolving on sparse data. Specifically, our approach propagates the target
depth from nearby depth measurements via a non-linear model, whose coefficients
are generated through a multi-layer perceptron conditioned on both
\emph{radiometric difference} and \emph{spatial distance}. By integrating
bilateral propagation with multi-modal fusion and depth refinement in a
multi-scale framework, our BP-Net demonstrates outstanding performance on both
indoor and outdoor scenes. It achieves SOTA on the NYUv2 dataset and ranks 1st
on the KITTI depth completion benchmark at the time of submission. Experimental
results not only show the effectiveness of bilateral propagation but also
emphasize the significance of early-stage propagation in contrast to the
refinement stage. Our code and trained models will be available on the project
page.
