论文标题
在动态环境中使用深SFM的多视图单眼深度和不确定性预测
Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments
论文作者
论文摘要
在动态环境中，单眼视频的深度和运动的3D重建是一个高度不良的问题，这是由于标准歧义投影到2D图像域时。在这项工作中，我们研究了此类环境中当前最新的（SOTA）深度视图系统的性能。我们发现，尽管没有对单个对象动作进行建模，但目前的监督方法出人意料地奏效，而是由于缺乏密集的地面真相数据而犯了系统的错误。为了在使用过程中检测到此类错误，我们将基于成本量的深度视频扩展到深度（DEEPV2D）框架\ cite {TEED2018 -DEEPV2D}，并具有学习的不确定性。我们的深度视频达到某些深度（DEEPV2CD）模型允许i）与当前的SOTA和II一起执行或更好地执行与天真的香农熵相比，获得更好的不确定性度量。我们的实验表明，基于不确定性的简单过滤策略可以显着减少系统错误。这会导致在场景的静态和动态部分上进行清洁的重建。
3D reconstruction of depth and motion from monocular video in dynamic environments is a highly ill-posed problem due to scale ambiguities when projecting to the 2D image domain. In this work, we investigate the performance of the current State-of-the-Art (SotA) deep multi-view systems in such environments. We find that current supervised methods work surprisingly well despite not modelling individual object motions, but make systematic errors due to a lack of dense ground truth data. To detect such errors during usage, we extend the cost volume based Deep Video to Depth (DeepV2D) framework \cite{teed2018deepv2d} with a learned uncertainty. Our Deep Video to certain Depth (DeepV2cD) model allows i) to perform en par or better with current SotA and ii) achieve a better uncertainty measure than the naive Shannon entropy. Our experiments show that a simple filter strategy based on the uncertainty can significantly reduce systematic errors. This results in cleaner reconstructions both on static and dynamic parts of the scene.
