Reconstructing depth information from images is one of the actively researched themes\ud in computer vision and its application involves most vision research areas from object\ud recognition to realistic visualisation. Amongst other useful vision-based reconstruction\ud techniques, this thesis extensively investigates the visual hull (VH) concept for volume\ud approximation and its robust surface modelling when various views of an object are\ud available. Assuming that multiple images are captured from a circular motion, projection\ud matrices are generally parameterised in terms of a rotation angle from a reference position\ud in order to facilitate the multi-camera calibration. However, this assumption is often\ud violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle\ud is hardly realisable. To address this problem, at first, this thesis proposes a calibration\ud method associated with the approximate circular motion.\ud With these modified projection matrices, a resulting VH is represented by a hierarchical\ud tree structure of voxels from which surfaces are extracted by the Marching\ud cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by\ud a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and\ud imperfect image processing or calibration result. To avoid this sensitivity, this thesis\ud proposes a robust surface construction algorithm which initially classifies local convex\ud regions from imperfect MC vertices and then aggregates local surfaces constructed by the\ud 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline\ud images to refine a coarse VH using an affine invariant region descriptor. This improves\ud the quality of VH when a small number of initial views is given.\ud In conclusion, the proposed methods achieve a 3D model with enhanced accuracy.\ud Also, robust surface modelling is retained when silhouette images are degraded by\ud practical noise
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