Abstract
We discuss two image-based 3D modeling methods based on a multiresolution evolution of a volumetric function′s level set. In the former method, the role of the level set implosion is to fuse ("sew" and "stitch") together several partial reconstructions (depth maps) into a closed model. In the later, the level set′s implosion is steered directly by the texture mismatch between views. Both solutions share the characteristic of operating in an adaptive multiresolution fashion, in order to boost up computational efficiency and robustness.
Highlights
Building a complete 3D model of objects from a sequence of images is a task of great difficulty, as it involves many complex steps such as camera motion tracking, image-based depth estimation, and global surface modeling
The level set’s implosion is steered directly by the texture mismatch between views. Both solutions share the characteristic of operating in an adaptive multiresolution fashion, in order to boost up computational efficiency and robustness
The method consists of assigning a luminance value to a generic pixel through the analysis of sign changes in the volumetric function φ along the associated optical ray: (i) if the volumetric function φ(x) does not change sign along the optical ray, we assign the pixel the background luminance; (ii) if φ(x) exhibits a sign change along the optical ray, the pixel will be assigned a value of luminance, computed using an appropriate radiometric model that accounts for the viewing direction and the normal to the front
Summary
Building a complete 3D model of objects from a sequence of images is a task of great difficulty, as it involves many complex steps such as camera motion tracking, image-based depth estimation, and global surface modeling. A 3D manifold can be generally defined and represented either explicitly, through a juxtaposition of overlapping 3D maps; or implicitly, as the set of points that satisfy a nonlinear constraint in the 3D space (a level set of a volumetric function) Using the former or the latter, surface representation has a substantial impact on how the image-based 3D modeling problem is posed. Some effective strategies have been proposed to overcome the inevitable computational costs associated to the intrinsic volumetric representation in image-based shape modeling Such solutions refer to two different approaches to image-based surface modeling: the first (indirect method) consists of constructing the global surface as 3D mosaic of simpler surface patches [15], while the second (direct method) skips the partial modeling step and uses the available images to steer the evolution of the level set toward the final surface shape [16].
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