Luc Van Gool

Abstract: We propose a method for computing the absolute distances to static obstacles using a single omnidirectional camera. The method is applied to mobile robots. We achieve this without restricting the application to predetermined translations or the use of artificial markers. In contrast to prior work, our method is able to build absolute scale 3D without the need of a known baseline length, traditionally acquired by odometry. Instead we use the ground plane assumption together with the camera system's height to determine the scale factor. Using only one omnidirectional camera our method is cheaper, more informative and more compact than the traditional methods for distance determination, especially when a robot is already equipped with a camera for e.g. navigation. It also provides more information since it determines distances in a 3D space instead of in one plane. The experiments show promising results. The algorithm is indeed capable of determining the distances in meters to features and obstacles and is able to locate all major obstacles in the scene.

TL;DR: A simple and flexible family of non-linear kernels which are arbitrary kernels in the index space of a data quantizer, i.e., piecewise constant similarities in the original feature space that grant access to Euclidean geometry for uncompressed features are introduced.

TL;DR: This work investigates the use of simple flash-based photography to capture an object's 3D shape and reflectance characteristics at the same time, based on the principles of Structure from Motion and Photometric Stereo.

TL;DR: In this paper, a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks is derived by introducing a learned error metric and a latent representation of the target image.