Abstract: Interesting visual simulations have been proposed for physical phenomena such as fire and smoke, but scant attention has been paid to reconstructing models of these phenomena from multiple images of real scenes. Here we study the problem of reconstructing three-dimensional models of fire from synchronized images or video sequences. Using a physically-based imaging model, we reduce the reconstruction of fire to the little-studied problem of computerized tomography with an extremely limited number of views (as few as two). We analyse the space of photo-consistent reconstructions, propose a novel reconstruction method for fire, and develop an image-based method for rendering the reconstructions. The reconstruction method involves parameterizing the fire with a linear superposition of Gaussians, estimating the density according to the input images, and applying a stochastic resampling scheme to refine the reconstruction. Experimental results are presented for a variety of synthetic and real fire datasets.