Adaptive grid optical tomography

Image-based modeling of semi-transparent, dynamic phenomena is a challenging task. We present an optical tomography method that uses an adaptive grid for the reconstruction of a three-dimensional density function from its projections. The proposed method is applied to reconstruct thin smoke and flames volumetrically from synchronized multi-video recordings. Our adaptive reconstruction algorithm computes a time-varying volumetric model, that enables the photorealistical rendering of the recorded phenomena from arbitrary viewpoints. In contrast to previous approaches we sample the underlying unknown, three-dimensional density function adaptively which enables us to achieve a higher effective resolution of the reconstructed models.