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The Visual Hull Concept for Silhouette-Based Image Understanding
February 1994 (vol. 16 no. 2)
pp. 150-162

Many algorithms for both identifying and reconstructing a 3-D object are based on the 2-D silhouettes of the object. In general, identifying a nonconvex object using a silhouette-based approach implies neglecting some features of its surface as identification clues. The same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object. This paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette-based image understanding. For this purpose, the geometric concept of visual hull of a 3-D object is introduced. This is the closest approximation of object S that can be obtained with the volume intersection approach; it is the maximal object silhouette-equivalent to S, i.e., which can be substituted for S without affecting any silhouette. Only the parts of the surface of S that also lie on the surface of the visual hull can be reconstructed or identified using silhouette-based algorithms. The visual hull depends not only on the object but also on the region allowed to the viewpoint. Two main viewing regions result in the external and internal visual hull. In the former case the viewing region is related to the convex hull of S, in the latter it is bounded by S. The internal visual hull also admits an interpretation not related to silhouettes. Algorithms for computing visual hulls are presented and their complexity analyzed. In general, the visual hull of a 3-D planar face object turns out to be bounded by planar and curved patches.

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Index Terms:
image reconstruction; silhouette-based image understanding; object identification; object reconstruction; nonconvex object; internal visual hull; external visual hull
Citation:
A. Laurentini, "The Visual Hull Concept for Silhouette-Based Image Understanding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 150-162, Feb. 1994, doi:10.1109/34.273735
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