The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of realworld illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.
ABSTRACT:In-situ calibration of structured light scanners in underwater environments is time-consuming and complicated. This paper presents a self-calibrating line laser scanning system, which enables the creation of dense 3D models with a single fixed camera and a freely moving hand-held cross line laser projector. The proposed approach exploits geometric constraints, such as coplanarities, to recover the depth information and is applicable without any prior knowledge of the position and orientation of the laser projector. By employing an off-the-shelf underwater camera and a waterproof housing with high power line lasers an affordable 3D scanning solution can be built. In experiments the performance of the proposed technique is studied and compared with 3D reconstruction using explicit calibration. We demonstrate that the scanning system can be applied to above-the-water as well as underwater scenes.
ABSTRACT:This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google's results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.
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