CS: Compressive Light Transport Sensing

Let's take a look at a very noteworthy contribution as it clearly has impact on solving the linear transport equation (an interest of mine).

Here is a new paper entitled Compressive Light Transport Sensing ( a previous technical report can be found on this site) by Pieter Peers, Dhruv Mahajan, Bruce Lamond, Abhijeet Ghosh, Wojciech Matusik and Ravi Ramamoorthi. The abstract reads:

In this article we propose a new framework for capturing light transport data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid mathematical framework to infer a sparse signal from a limited number of nonadaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting interpixel coherency relations. Additionally, we design new nonadaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed high-resolution reflectance fields for image-based relighting.

The teaser photo shown above has the following caption:

Three scenes captured using only 1000 non-adaptive compressive measurements, and relit using a novel conditions. The 128x128 reflectance functions of each camera pixel is reconstructed using our hierarhical reconstruction algorithm using 128 Haar wavelet coefficients per function from the observed compressive measurements.

This is outstanding.