Precomputed ROMP for light transport acquisition

In this article, we propose an efficient and accurate compressive sensing-based method for estimating the light transport characteristics of real world scenes. Although compressive sensing allows efficient estimation of a high-dimension signal with a sparse or near-to-sparse representation from a small number of samples, the computational cost of the compressive sensing in estimating the light transport characteristics is relatively high. Moreover, these methods require a relatively smaller number of images compared with other techniques although they still need 500-1000 images to estimate an accurate light transport matrix. Our proposed method - precomputed ROMP (Regularized Orthogonal Matching Pursuit) - improves the performance of the compressive sensing by providing an appropriate initial state, which allows us to more accurately estimate the matrix with fewer images. This improvement was achieved through two steps: 1) pseudo-single pixel projection by multi-line projection - measuring coarse light transport characteristics to utilize them as an initial state, 2) ROMP with initial signal - refining coarse light transport characteristics with the compressive sensing theory with the initial signal. Precomputed ROMP was carried out by parallel processing. With these two steps, we were able to estimate the light transport characteristics more accurately, much faster, and with a lesser number of images.