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The depth completion task aims to complete a per-pixel dense depth map from asparse depth map. In this paper, we propose an efficient least square baseddepth-independent method to complete the sparse depth map utilizing the RGBimage and the sparse depth map in two independent stages. In this way can wedecouple the neural network and the sparse depth input, so that when somefeatures of the sparse depth map change, such as the sparsity, our method canstill produce a promising result. Moreover, due to the positional encoding andlinear procession in our pipeline, we can easily produce a super-resolutiondense depth map of high quality. We also test the generalization of our methodon different datasets compared to some state-of-the-art algorithms. Experimentson the benchmark show that our method produces competitive performance.

Depth-Independent Depth Completion via Least Square Estimation