Abstract:This paper explores the possibility of acquiring inverse light transport directly. The current strategy of obtaining an inverse light transport matrix involves two steps: First, acquire the forward light transport matrix (f-LTM) and then calculate the inverse of the f-LTM. Both steps of the strategy requires considerable computational power. In addition to computational cost, the measurement error incurred at the first step inevitably propagates to or potentially gets amplified in the matrix inversion step.In … Show more
“…Finally, while it is not closely related to dual photography, we note that the idea of direct reconstruction using compressive sensing was also exploited to obtain the inverse light transport [7].…”
Dual photography is a well-known application of light transport acquired by a projector-camera system. By applying compressive sensing, compressive dual photography [1] is a fast approach to acquire the light transport for dual photography. However, the reconstruction step in compressive dual photography can still take several hours before dual images can be synthesized because the entire light transport needs to be reconstructed from measured data. In this paper, we present a novel reconstruction approach that can directly and progressively synthesize dual images from measured data without the need of first reconstructing the light transport. We show that our approach can produce high-quality dual images in the order of minutes using only a thousand of samples. Our approach is most useful for previewing a few dual images, e.g., during light transport acquisition. As a by-product, our method can also perform low-resolution relighting of dual images. We also hypothesize that our method is applicable to reconstructing dual images in a single projector -multiple cameras system.
“…Finally, while it is not closely related to dual photography, we note that the idea of direct reconstruction using compressive sensing was also exploited to obtain the inverse light transport [7].…”
Dual photography is a well-known application of light transport acquired by a projector-camera system. By applying compressive sensing, compressive dual photography [1] is a fast approach to acquire the light transport for dual photography. However, the reconstruction step in compressive dual photography can still take several hours before dual images can be synthesized because the entire light transport needs to be reconstructed from measured data. In this paper, we present a novel reconstruction approach that can directly and progressively synthesize dual images from measured data without the need of first reconstructing the light transport. We show that our approach can produce high-quality dual images in the order of minutes using only a thousand of samples. Our approach is most useful for previewing a few dual images, e.g., during light transport acquisition. As a by-product, our method can also perform low-resolution relighting of dual images. We also hypothesize that our method is applicable to reconstructing dual images in a single projector -multiple cameras system.
“…Bai et al [2010] apply Jacobi iterative method to inversion of T and introduce fast computational algorithm using Monte Carlo and wavelet-based methods in forward rendering. Chu et al [2011] use the compressive inverse theory to acquire the inverse light transport matrix directly, and reduces both computational error and cost.…”
We present a fast perceptual radiometric compensation method for inter-reflection in immersive projection environment. Radiometric compensation is the inverse process of light transport. As light transport process can be described by a matrix-vector multiplication equation, radiometric compensation for inter-reflection can be achieved by solving the equation to get the vector, during which matrix inversion should be computed. As the dimensions of the matrix are equivalent to the resolution of images, such matrix inversion is both time and storage consuming. Unlike previous methods, our method adopts projector-camera system to simulate the inversion, and treats the compensation as a non-linear optimization problem which is formulated from full light transport matrix and non-linear color space conversion. To make physical multiplication simulation more practical, the method adjusts the range of projector-camera system adaptively and reduces the high-frequency errors caused by clipping error and measured error to make the compensated results smoother. We implement an immersive projection display prototype. The experiments show that our method achieves better results compared with the previous method.
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