Find your Way by Observing the Sun and Other Semantic Cues

In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information

Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"

Recovering shadows is an important step for many vision algorithms. Current approaches that work with time-lapse sequences are limited to simple thresholding heuristics. We show these approaches only work with very careful tuning of parameters, and do not work well for long-term time-lapse sequences taken over the span of many months. We introduce a parameter-free expectation maximization approach which simultaneously estimates shadows, albedo, surface normals, and skylight. This approach is more accurate than previous methods, works over both very short and very long sequences, and is robust to the effects of nonlinear camera response. Finally, we demonstrate that the shadow masks derived through this algorithm substantially improve the performance of sun-based photometric stereo compared to earlier shadow mask estimation

Camera Calibration And Geo-Location Estimation From Two Shadow Trajectories

The position of a world point\u27s solar shadow depends on its geographical location, the geometrical relationship between the orientation of the sunshine and the ground plane where the shadow casts. This paper investigates the property of solar shadow trajectories on a planar surface and shows that camera parameters, latitude, longitude and shadow casting objects\u27 relative height ratios can be estimated from two observed shadow trajectories. One contribution is to recover the horizon line and metric rectification matrix from four observations of two shadow tips. The other contribution is that we use the design of an analemmatic sundial to get the shadow conic and furthermore recover the camera\u27s geographical location. The proposed method does not require the shadow casting objects or a vertical object to be visible in the recovery of camera calibration. This approach is thoroughly validated on both synthetic and real data, and tested against various sources of errors including noise, number of observations, objects locations, and camera orientations. We also present applications to image-based metrology. © 2010 Elsevier Inc. All rights reserved

Camera calibration and geo-location estimation from two shadow trajectories

The position of a world point\u27s solar shadow depends on its geographical location, the geometrical relationship between the orientation of the sunshine and the ground plane where the shadow casts. This paper investigates the property of solar shadow trajectories on a planar surface and shows that camera parameters, latitude, longitude and shadow casting objects\u27 relative height ratios can be estimated from two observed shadow trajectories. One contribution is to recover the horizon line and metric rectification matrix from four observations of two shadow tips. The other contribution is that we use the design of an analemmatic sundial to get the shadow conic and furthermore recover the camera\u27s geographical location. The proposed method does not require the shadow casting objects or a vertical object to be visible in the recovery of camera calibration. This approach is thoroughly validated on both synthetic and real data, and tested against various sources of errors including noise, number of observations, objects locations, and camera orientations. We also present applications to image-based metrology. (C) 2010 Elsevier Inc. All rights reserved