  The depth completion task aims to complete a per-pixel dense depth map from a
sparse depth map. In this paper, we propose an efficient least square based
depth-independent method to complete the sparse depth map utilizing the RGB
image and the sparse depth map in two independent stages. In this way can we
decouple the neural network and the sparse depth input, so that when some
features of the sparse depth map change, such as the sparsity, our method can
still produce a promising result. Moreover, due to the positional encoding and
linear procession in our pipeline, we can easily produce a super-resolution
dense depth map of high quality. We also test the generalization of our method
on different datasets compared to some state-of-the-art algorithms. Experiments
on the benchmark show that our method produces competitive performance.
