Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City

Abstract

Abstract:In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG ("what you see is what you get") urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.

System Design, Calibration and Performance Analysis of a Novel 360° Stereo Panoramic Mobile Mapping System

“…Moreover, the calibration accuracy has been increased significantly compared to Burkhard et al (2012). Furthermore, in order to generate geospatial 3D images and 3D image spaces (Nebiker et al, 2015) from fisheye stereo images, an image processing workflow was developed according to Abraham & Förstner (2005). We evaluated the accuracies in a challenging urban area in the city centre of Basel using our 360° stereo panorama mobile mapping system.…”

“…for infrastructure management or urban planning. As suggested by Nebiker et al (2015), a geospatial 3D image consists of a georeferenced RGB image with additional channels supporting depth and quality information, ideally for each pixel. Ideally, 3D images are derived from stereo imagery using dense image matching -in this case from raw equidistant fisheye stereo image pairs -in order to obtain very dense depth representations and to ensure the spatial and temporal coherence of radiometric and depth data.…”

A Systematic Comparison of Direct and Image-Based Georeferencing in Challenging Urban Areas

“…Since we newly set tie point accuracy to 0.3 pixel and defined 0.5 pixel for image observations to ground control points, also sequences acquired in July 2014 were reprocessed which led to slightly different results compared to Cavegn et al (2015) and to Nebiker et al (2015). Overall RMSE values of 0.42-0.89 pixel were computed, 0.15-0.21 pixel for tie points and 0.81-1.08 pixel for ground control points (see Table 5).…”

Design and Implementation of a Novel Portable 360° Stereo Camera System With Low-Cost Action Cameras

“…This is especially true for users unfamiliar with point clouds and for complex scanning objects such as indoor environments. In contrast to depth values captured with LiDAR, the depth from dense image matching ensures the spatial and temporal coherence of radiometric and depth data of the 3D imagery (Nebiker et al, 2015). The 3D information can hereby be derived directly from the images.…”