它在这个基准之外工作吗？引入刚体深度构造器工具，在刚体场景中为大众构造深度验证数据集
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses
摘要：
我们提出了一个协议来构建自己的深度验证数据集导航。该协议被称为RDC for Rigid Depth Constructor，旨在比现有技术更容易获得和更便宜，只需要一个摄像头和一个激光雷达传感器即可开始。我们还开发了一个测试套件，以获得有见地的信息，从评估的算法。最后，我们以无人机视频为例，测试了最初在KITTI上测试的两种深度算法，并表明无人机上下文与车内视频有很大不同。这表明，单个上下文基准不应被认为是可靠的，并且在开发深度估计算法时，应该在最适合特定需求的数据集上对其进行基准测试，这通常意味着创建一个全新的数据集。在本文中，我们提供了一个开源实现的工具，并计划使其尽可能用户友好，即使是小团队也可以创建深度数据集。我们的主要贡献如下：我们提出了一个完整的，开源的，几乎完全自动化的软件应用程序，用于创建具有密集注释深度的验证数据集，适用于各种图像，视频和范围数据。它包括用于使数据集适应特定验证需求的选择工具，以及用于其他数据集格式的转换工具。使用这个应用程序，我们提出了两个新的真实数据集，室外和室内，易于使用的无人机导航上下文。最后，作为例子，我们展示了两个深度预测算法的评估，使用一个全面的（例如，基于分布的）指标的集合。
英文摘要：
We present a protocol to construct your own depth validation dataset for navigation. This protocol, called RDC for Rigid Depth Constructor, aims at being more accessible and cheaper than already existing techniques, requiring only a camera and a Lidar sensor to get started. We also develop a test suite to get insightful information from the evaluated algorithm. Finally, we take the example of UAV videos, on which we test two depth algorithms that were initially tested on KITTI and show that the drone context is dramatically different from in-car videos. This shows that a single context benchmark should not be considered reliable, and when developing a depth estimation algorithm, one should benchmark it on a dataset that best fits one's particular needs, which often means creating a brand new one. Along with this paper we provide the tool with an open source implementation and plan to make it as user-friendly as possible, to make depth dataset creation possible even for small teams. Our key contributions are the following: We propose a complete, open-source and almost fully automatic software application for creating validation datasets with densely annotated depth, adaptable to a wide variety of image, video and range data. It includes selection tools to adapt the dataset to specific validation needs, and conversion tools to other dataset formats. Using this application, we propose two new real datasets, outdoor and indoor, readily usable in UAV navigation context. Finally as examples, we show an evaluation of two depth prediction algorithms, using a collection of comprehensive (e.g. distribution based) metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
