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.

翻译：完成深度任务的目的是从稀薄的深度地图上完成每像素密度深度地图。 在本文中,我们提出一种效率最低的平方基深度独立方法,以利用RGB图像和稀薄深度地图在两个独立阶段完成稀薄深度地图。 这样我们可以将神经网络和稀薄深度输入分离出来,这样当稀薄深度地图变化的某些特征,如宽度,我们的方法仍然能够产生一个大有希望的结果。 此外,由于我们管道中的定位编码和线性流程,我们很容易生成一个超分辨率密度深度高的高质量深度地图。我们还测试我们的方法在不同的数据集上与一些最先进的算法相比的通用性。 基准实验显示,我们的方法产生竞争性的性能。