Image-Based Rendering of LOD1 3D City Models for traffic-augmented Immersive Street-view Navigation

Abstract

Abstract. It may be argued that urban areas may now be modeled with sufficient details for realistic fly-through over the cities at a reasonable price point. Modeling cities at the street level for immersive street-view navigation is however still a very expensive (or even impossible) operation if one tries to match the level of detail acquired by street-view mobile mapping imagery. This paper proposes to leverage the richness of these street-view images with the common availability of nation-wide LOD1 3D city models, using an image-based rendering technique : projective multi-texturing. Such a coarse 3D city model may be used as a lightweight scene proxy of approximate coarse geometry. The images neighboring the interpolated viewpoint are projected onto this scene proxy using their estimated poses and calibrations and blended together according to their relative distance. This enables an immersive navigation within the image dataset that is perfectly equal to – and thus as rich as – original images when viewed from their viewpoint location, and which degrades gracefully in between viewpoint locations. Beyond proving the applicability of this preprocessing-free computer graphics technique to mobile mapping images and LOD1 3D city models, our contributions are three-fold. Firstly, image distortion is corrected online in the GPU, preventing an extra image resampling step. Secondly, externally-computed binary masks may be used to discard pixels corresponding to moving objects. Thirdly, we propose a shadowmap-inspired technique that prevents, at marginal cost, the projective texturing of surfaces beyond the first, as seen from the projected image viewpoint location. Finally, an augmented visualization application is introduced to showcase the proposed immersive navigation: images are unpopulated from vehicles using externally-computed binary masks and repopulated using a 3D visualization of a 2D traffic simulation.