|Compressive Light Transport Sensing|
|ACM Transactions on Graphics, Volume 28, Issue 1 (January 2009)|
|ICT Technical Report, ICT-TR-05-2008, May 2008|
|Pieter Peers1 Dhruv K. Mahajan2 Bruce
Lamond1 Abhijeet Ghosh1
Wojciech Matusik4 Ravi Ramamoorthi2,3 Paul Debevec1
|University of Southern California, Institute for Creative Technologies1|
University of Berkely3
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.
In this paper 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 non-adaptive 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 inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed highresolution reflectance fields for image-based relighting.
ACM Transactions on Graphics Paper
ICT Technical Report (May 2008)
Oops: An unfortunate error crept into both the TOG paper and the technical report. Eq. 15 is obviously incorrect. However, one can still apply Eq. 16 to compute a sparse representation of the difference.
Concurrent Related Work:
We have recently become aware of concurrent and independent work performed by Pradeep Sen and Soheil Darabi at the University of New Mexico (link) that applies compressive sensing to Dual Photography. This work tackles the problem of measuring high resolution reflectance fields in a similar manner. The main difference is that they directly apply Bernoulli noise measurement patterns that work well in a Dual Photography setup.
- Light Stage 1:
- Light Stage 2:
- A Photometric Approach to Digitizing Cultural Artifacts, VAST 2001
- Animatable Facial Reflectance Fields, EGSR 2004
- Reflectance Field Rendering of Human Faces for "Spider-Man 2", SIGGRAPH 2004 Sketch
- Light Stage 3:
- Light Stage 5:
- Postproduction Re-Illumination of Live Action Using Interleaved Lighting, SIGGRAPH 2004 Poster
- Performance Geometry Capture for Spatially Varying Relighting, SIGGRAPH 2005 Sketch
- Light Stage 6:
- Relighting Human Locomotion with Flowed Reflectance Fields, EGSR 2006 Paper
- Relighting Human Locomotion with Flowed Reflectance Fields, SIGGRAPH 2006 Sketch
- Light Stage Data Gallery.
- Reflectance Transfer:
- Post-production Facial Performance Relighting using Reflectance Transfer, SIGGRAPH 2007 Paper
- Dual Light Stage:
- A Dual Light Stage, EGSR 2005 Paper