论文标题
部分可观测时空混沌系统的无模型预测
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image
论文作者
论文摘要
在过去的几年中，360°图像广泛可用。本文提出了一种在开放环境下的单个360°图像深度预测的新技术。出于两个原因，从360°单图中的深度预测并不容易。一个是监督数据集的限制 - 当前可用的数据集仅限于室内场景。另一个是由等应角投影格式（ERP）引起的问题，该格式通常用于360°图像，它们是坐标和失真。现有的方法只有一种使用Cube Map投影来产生六个透视图像，并使用电影图片进行自我监督的学习来透视深度预测来解决这些问题。与现有方法不同，我们直接使用ERP格式。我们提出了一个直接使用ERP与对应关系和失真感知的UP采样模块的框架，以处理与ERP相关的问题，并扩展了开放环境的自我监督学习方法。对于实验，我们首先构建了一个用于评估的数据集，并定量评估室外场景中的深度预测。我们表明，它的表现优于最先进的技术
360° images are widely available over the last few years. This paper proposes a new technique for single 360° image depth prediction under open environments. Depth prediction from a 360° single image is not easy for two reasons. One is the limitation of supervision datasets - the currently available dataset is limited to indoor scenes. The other is the problems caused by Equirectangular Projection Format (ERP), commonly used for 360° images, that are coordinate and distortion. There is only one method existing that uses cube map projection to produce six perspective images and apply self-supervised learning using motion pictures for perspective depth prediction to deal with these problems. Different from the existing method, we directly use the ERP format. We propose a framework of direct use of ERP with coordinate conversion of correspondences and distortion-aware upsampling module to deal with the ERP related problems and extend a self-supervised learning method for open environments. For the experiments, we firstly built a dataset for the evaluation, and quantitatively evaluate the depth prediction in outdoor scenes. We show that it outperforms the state-of-the-art technique
