Abstract
We present a novel GPU-accelerated per-pixel inverse rendering optimization algorithm based on Particle Swarm Optimization (PSO). Our algorithm estimates the per-pixel scene attributes—including reflectance properties—of a 3D model, and is fast enough to do in situ visualization of the optimization in real-time. The algorithm’s high parallel efficiency is demonstrated through our GPU/GLSL shader implementation of the method. IRPSO is validated experimentally on simulated ground truth images, while a suite of tests performed on the University of Southern California’s High Performance Computing Center cluster provides strong evidence that our method can scale to larger, more difficult inverse rendering problems.
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Acknowledgments
This work was developed from a final project for CSCI 596 (scientific computing and visualization) course at the University of Southern California. Integration of research and education in CSCI 596 was supported by the National Science Foundation, Grant No. 1508131. We also thank the USC Institute for Creative Technologies for the support of the work.
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Nagano , K., Collins, T., Chen, CA. et al. Massively parallel inverse rendering using Multi-objective Particle Swarm Optimization. J Vis 20, 195–204 (2017). https://doi.org/10.1007/s12650-016-0369-3
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DOI: https://doi.org/10.1007/s12650-016-0369-3