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Prioritized 3D scene reconstruction and rate-distortion efficient representation for video sequences

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2007
İmre, Evren
In this dissertation, a novel scheme performing 3D reconstruction of a scene from a 2D video sequence is presented. To this aim, first, the trajectories of the salient features in the scene are determined as a sequence of displacements via Kanade-Lukas-Tomasi tracker and Kalman filter. Then, a tentative camera trajectory with respect to a metric reference reconstruction is estimated. All frame pairs are ordered with respect to their amenability to 3D reconstruction by a metric that utilizes the baseline distances and the number of tracked correspondences between the frames. The ordered frame pairs are processed via a sequential structure-from-motion algorithm to estimate the sparse structure and camera matrices. The metric and the associated reconstruction algorithm are shown to outperform their counterparts in the literature via experiments. Finally, a mesh-based, rate-distortion efficient representation is constructed through a novel procedure driven by the error between a target image, and its prediction from a reference image and the current mesh. At each iteration, the triangular patch, whose projection on the predicted image has the largest error, is identified. Within this projected region and its correspondence on the reference frame, feature matches are extracted. The pair with the least conformance to the planar model is used to determine the vertex to be added to the mesh. The procedure is shown to outperform the dense depth-map representation in all tested cases, and the block motion vector representation, in scenes with large depth range, in rate-distortion sense.