2016 · 18 citations · 15 references
EngineeringGeometryField RoboticsStereo ImagingDense 3DImage AnalysisSpherical CameraSpherical ImagesComputational ImagingPhotometric StereoKinematicsComputational GeometryGeometric ModelingMachine VisionStructure From MotionMedical Image ComputingComputer VisionNatural SciencesComputer Stereo Vision3D ReconstructionMulti-view GeometryStereoscopic Processing
In this research, a technique for dense 3D reconstruction from two spherical images clicked at displaced positions near a large structure is formulated. The technique is based on the use of global variational information i.e. the dense optical flow field in a unique rectification based refinement of the epipolar geometry between the two images in a vertically displaced orientation. A non-linear minimization is used to globally align the 2D equirectangular optical flow field (i.e. pixel displacements). The magnitude component of the resultant optical flow field can directly be converted to a dense 3D reconstruction. Thus, the epipolar geometry as well as the 3D structure can be estimated in a single minimization. This method could be useful in measurement and reconstruction of large structures such as bridges, etc. using a robot equipped with a spherical camera, thus helping in their inspection and maintenance.
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Martin A. Fischler, Robert C. Bolles · Communications of the ACM · 1981 · 24.9K citations · Full text
Engineering, Random Sample Consensus, Sampling Technique +20
DeepFlow: Large Displacement Optical Flow with Deep Matching
Philippe Weinzaepfel, Jérôme Revaud, Zaïd Harchaoui et al. · 2013 · 986 citations · Full text