  Depth completion involves estimating a dense depth image from sparse depth
measurements, often guided by a color image. While linear upsampling is
straight forward, it results in artifacts including depth pixels being
interpolated in empty space across discontinuities between objects. Current
methods use deep networks to upsample and "complete" the missing depth pixels.
Nevertheless, depth smearing between objects remains a challenge. We propose a
new representation for depth called Depth Coefficients (DC) to address this
problem. It enables convolutions to more easily avoid inter-object depth
mixing. We also show that the standard Mean Squared Error (MSE) loss function
can promote depth mixing, and thus propose instead to use cross-entropy loss
for DC. With quantitative and qualitative evaluation on benchmarks, we show
that switching out sparse depth input and MSE loss with our DC representation
and cross-entropy loss is a simple way to improve depth completion performance,
and reduce pixel depth mixing, which leads to improved depth-based object
detection.
