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We present a protocol to construct your own depth validation dataset fornavigation. This protocol, called RDC for Rigid Depth Constructor, aims atbeing more accessible and cheaper than already existing techniques, requiringonly a camera and a Lidar sensor to get started. We also develop a test suiteto get insightful information from the evaluated algorithm. Finally, we takethe example of UAV videos, on which we test two depth algorithms that wereinitially tested on KITTI and show that the drone context is dramaticallydifferent from in-car videos. This shows that a single context benchmark shouldnot 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 providethe tool with an open source implementation and plan to make it asuser-friendly as possible, to make depth dataset creation possible even forsmall teams. Our key contributions are the following: We propose a complete,open-source and almost fully automatic software application for creatingvalidation datasets with densely annotated depth, adaptable to a wide varietyof image, video and range data. It includes selection tools to adapt thedataset to specific validation needs, and conversion tools to other datasetformats. Using this application, we propose two new real datasets, outdoor andindoor, readily usable in UAV navigation context. Finally as examples, we showan evaluation of two depth prediction algorithms, using a collection ofcomprehensive (e.g. distribution based) metrics.

Does it work outside this benchmark? Introducing the Rigid Depth  Constructor tool, depth validation dataset construction in rigid scenes for  the masses