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The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by(More)
Automatic image-based-modeling usually has two steps: Structure from Motion (SfM) and the estimation of a triangulated surface. The former provides camera poses and a sparse point cloud. The latter usually involves dense stereo. From the computational standpoint, it would be nice to avoid dense stereo and estimate the surface from the sparse cloud directly.(More)
The automatic surface reconstruction from an image sequence is still an active research topic. Recently, a method was designed to reconstruct a 2-manifold surface from the sparse cloud of points generated by Structure-from-Motion (SfM). This method reconstructs outdoor scenes from hundreds of catadioptric images. It is based on sculpting the 3d Delaunay(More)
An image processing method is proposed to realize polarizer-free imaging of liquid crystal lens. Images I(l) and I(nl) are captured sequentially in the lens and non-lens states of the LC lens, respectively, and are used to generate a final high contrast image. The proposal is tested by experiments. Clear and well focused images are obtained, even though no(More)
The automatic 3d modeling of an environment using images is still an active topic in Computer Vision. Standard methods have three steps: moving a camera in the environment to take an image sequence, reconstructing the geometry of the environment, and applying a dense stereo method to obtain a surface model of the environment. In the second step, interest(More)
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