论文标题
通过最小平方估计，无独立的深度完成
Depth-Independent Depth Completion via Least Square Estimation
论文作者
论文摘要
深度完成任务旨在从稀疏深度图中完成单像素密集的深度图。在本文中，我们提出了一种有效的基于最小平方的深度独立的方法，以在两个独立的阶段中利用RGB图像和稀疏深度图完成稀疏的深度图。通过这种方式，我们可以将神经网络和稀疏深度输入分解，以便当稀疏深度图的某些特征（例如稀疏性）的某些特征时，我们的方法仍然可以产生有希望的结果。此外，由于管道中的位置编码和线性游行，我们可以轻松地生成高质量的超分辨率密度深度图。与某些最先进的算法相比，我们还测试了不同数据集上方法的概括。基准上的实验表明，我们的方法会产生竞争性能。
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.
