This paper considers the problem of reconstructing visually realistic 3D models of fire from a very small set of simultaneous views (even two). By modeling fire as a semi-transparent 3D density field, we show that fire reconstruction is equivalent to a severely under-constrained computerized tomography problem, for which traditional methods break down. Our approach is based on the observation that every pair of photographs of a semi-transparent scene defines a unique density field, called a Flame Sheet, that (1) concentrates all its density on one connected, semitransparent surface, (2) reproduces the two photos exactly, and (3) is the most spatially-coherent density field that does so. From this observation, we reduce fire reconstruction to the convex combination of sheet-like density fields, each of which is derived from the Flame Sheet of two input photos. Experimental results suggest that this method enables highquality view extrapolation without over-fitting artifacts. This paper describes a PDE-based method for dense depth extraction from multiple wide-baseline images. Emphasis lies on the usage of only a small amount of images. The integration of these multiple wide-baseline views is guided by the relative confidence that the system has in the matching to different views. This weighting is fine-grained in that it is determined for every pixel at every iteration. Reliable information spreads fast at the expense of less reliable data, both in terms of spatial communications within a view and in terms of information exchange between the views. Changes in intensity between images can be handled in a similar fine grained fashion.
In 2003, Hasinoff and Kutulakos REF presented a photo-consistent reconstruction method which reduces fire reconstruction to the convex combination of sheet-like density fields.
Photo-consistent 3D fire by Flame-Sheet decomposition
Proceedings Ninth IEEE International Conference on Computer Vision
Proceedings Ninth IEEE International Conference on Computer Vision
Computer Science