Single View Reflectance Capture using Multiplexed
Scattering and Time-of-flight Imaging

Nikhil Naik1 Shuang Zhao2 Andreas Velten1 Ramesh Raskar1 Kavita Bala2
1MIT Media Lab          2Cornell University



This paper introduces the concept of time-of-flight reflectance estimation, and demonstrates a new technique that allows a camera to rapidly acquire reflectance properties of objects from a single viewpoint, over relatively long distances and without encircling equipment. We measure material properties by indirectly illuminating an object by a laser source, and observing its reflected light indirectly using a time-of-flight camera. The configuration collectively acquires dense angular, but low spatial sampling, within a limited solid angle range - all from a single viewpoint. Our ultra-fast imaging approach captures space-time "streak images" that can separate out different bounces of light based on path length. Entanglements arise in the streak images mixing signals from multiple paths if they have the same total path length. We show how reflectances can be recovered by solving for a linear system of equations and assuming parametric material models; fitting to lower dimensional reflectance models enables us to disentangle measurements.

We demonstrate proof-of-concept results of parametric reflectance models for homogeneous and discretized heterogeneous patches, both using simulation and experimental hardware. As compared to lengthy or highly calibrated BRDF acquisition techniques, we demonstrate a device that can rapidly, on the order of seconds, capture meaningful reflectance information. We expect hardware advances to improve the portability and speed of this device.






We acknowledge Prof. Moungi Bawendi for making available his equipment and lab space. We would like to thank Rohit Pandharkar and Jason Boggess for their help at different stages of the project. The renderings of spheres are generated using the Mitsuba physically based renderer. Funding was provided by the National Science Foundation under awards CCF-0644175, CCF-0811680 and IIS-1011919, and an Intel PhD Fellowship. Ramesh Raskar was supported by an Alfred P. Sloan Research Fellowship.

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