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Terrestrial geolocalized images are nowadays widely used on the Internet, mainly in urban areas, through immersion services such as Google Street View. On the long run, we seek to enhance the visualization of these images; for that purpose, radiometric corrections must be performed to free them from illumination conditions at the time of acquisition. Given the simultaneously acquired 3D geometric model of the scene with LIDAR or vision techniques, we face an inverse problem where the illumination and the geometry of the scene are known and the reflectance of the scene is to be estimated. Our main contribution is the introduction of a symbolic ray-tracing rendering to generate parametric images, for quick evaluation and comparison with the acquired images. The proposed approach is then based on an iterative estimation of the reflectance parameters of the materials, using a single rendering pre-processing. We validate the method on synthetic data with linear BRDF models and discuss the limitations of the proposed approach with more general non-linear BRDF models.

The knowledge of the sky illumination is important for radiometric corrections and for computer graphics applications such as relighting or augmented reality. We propose an approach to compute environment maps, representing the sky radiance, from a set of ground-based images acquired by a panoramic acquisition system, for instance a mobile-mapping system. These images can be affected by important radiometric artifacts, such as bloom or overexposure. A Perez radiance model is estimated with the blue sky pixels of the images, and used to compute additive corrections in order to reduce these radiometric artifacts. The sky pixels are then aggregated in an environment map, which still suffers from discontinuities on stitching edges. The influence of the quality of estimated sky radiance on the simulated light signal is measured quantitatively on a simple synthetic urban scene; in our case, the maximal error for the total sensor radiance is about 10%.