2021-06-17, Wahbeh, Wissam, Ammann, Manuela, Nebiker, Stephan, van Eggermond, Michael, Erath, Alexander, Wahbeh, Wissam

With this paper, we present a novel approach for efficiently creating reality-based, high-fidelity urban 3D models for interactive VR cycling simulations. The foundation of these 3D models is accurately georeferenced street-level imagery, which can be captured using vehicle-based or portable mapping platforms. Depending on the desired type of urban model, the street-level imagery is either used for semi-automatically texturing an existing city model or for automatically creating textured 3D meshes from multi-view reconstructions using commercial off-the-shelf software. The resulting textured urban 3D model is then integrated with a real-time traffic simulation solution to create a VR framework based on the Unity game engine. Subsequently, the resulting urban scenes and different planning scenarios can be explored on a physical cycling simulator using a VR helmet or viewed as a 360-degree or conventional video. In addition, the VR environment can be used for augmented reality applications, e.g., mobile augmented reality maps. We apply this framework to a case study in the city of Berne to illustrate design variants of new cycling infrastructure at a major traffic junction to collect feedback from practitioners about the potential for practical applications in planning processes.

2021, Wahbeh, Wissam, Ammann, Manuela, Nebiker, Stephan, van Eggermond, Michael, Erath, Alexander

With this paper, we present a novel approach for efficiently creating reality-based, high-fidelity urban 3D models for interactive VR cycling simulations. The foundation of these 3D models is accurately georeferenced street-level imagery, which can be captured using vehicle-based or portable mapping platforms. Depending on the desired type of urban model, the street-level imagery is either used for semi-automatically texturing an existing city model or for automatically creating textured 3D meshes from multi-view reconstructions using commercial off-the-shelf software. The resulting textured urban 3D model is then integrated with a real-time traffic simulation solution to create a VR framework based on the Unity game engine. Subsequently, the resulting urban scenes and different planning scenarios can be explored on a physical cycling simulator using a VR helmet or viewed as a 360-degree or conventional video. In addition, the VR environment can be used for augmented reality applications, e.g., mobile augmented reality maps. We apply this framework to a case study in the city of Berne to illustrate design variants of new cycling infrastructure at a major traffic junction to collect feedback from practitioners about the potential for practical applications in planning processes.

2021, Nebiker, Stephan, Meyer, Jonas, Blaser, Stefan, Ammann, Manuela, Rhyner, Severin Eric

A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.