最小二乗推定による深度非依存の深度補完
Depth-Independent Depth Completion via Least Square Estimation
RGB画像と疎な深度マップを独立に処理し、最小二乗推定で密な深度マップを生成する手法を提案。疎性の変化に頑健で高品質な超解像深度補完を実現。
著者: Xianze Fang, Yunkai Wang, Zexi Chen, Yue Wang, Rong Xiong
分類: cs.CV, cs.RO
原文アブストラクト
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.
