DTIC-PLCT-2025-30 - Medium support technician: Computer Graphics (Rendering) Software Developer Specialist
hace 7 días
Project description:
Physically-based rendering (PBR) requires an accurate reproduction of the physical behavior of light and its interaction with the surface materials, which is formalized by the rendering equation. The typical approach to solve the illumination integral is by resorting to Classical Monte Carlo (CMC), thanks to its simplicity and ease of implementation. However, CMC requires a large number of samples to obtain an accurate estimate, even when coupled with variance reduction techniques. To address this issue several methods have been proposed in the literature, among which Bayesian Monte Carlo (BMC) and other Gaussian Process-based approaches with promising results. Nevertheless, the use of BMC in CG is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. This research project (named A-BMC) proposes to investigate and develop efficient solutions to generalize the application of this very promising technique to PBR.
Main research field: Computer Graphics (Physically-based Rendering), Ray-Tracing and C++ Development
Tasks to be performed:
The main task is to develop a C++ based Gaussian Process library interoperable with the Mitsuba ray-tracer. Such a library should be tailored to the needs of physically-based rendering, and be designed to easily incorporate the algorithms developed in the three research lines of the project. More specifically, support and active contribution to the following scientific activities, structured per research line of the project, is expected:
Research Line 1 (Progressive Bayesian Monte Carlo):
Support the implementation, in the Mitsuba raytracer, of a C++ solution for progressive Bayesian Monte Carlo integration, based on an existing Matlab/Python prototype. This activity is related to task T1.5 of A-BMC.
Fine-tune the resulting implementation. This activity is related to task T1.6 of A-BMC.
Research Line 2 (Active Sampling):
Support the implementation, in the Mitsuba raytracer, of a C++ solution for Active Sampling in the context of Bayesian Monte Carlo integration, based on an existing Matlab/Python prototype. This activity is related to task T2.5 of A-BMC)
Fine-tune the resulting implementation. This activity is related to task T2.6 of A-BMC)
Research Line 3 (Sharing Information Between Estimates):
Support the implementation, in the Mitsuba raytracer, of a C++ based solution for Efficient Multiple Inference, based on an existing Matlab/Python prototype. This activity is related to task T2.5 of A-BMC.
Fine-tune the resulting implementation. This activity is related to task T2.6 of A-BMC.
Group and complement: Group 2 + Level g
Dedication and working hours: Part time (30h/week).
Planned remuneration approx: 36.792,04 € gross per year
Financing fund: Proyecto PRESP MICIU/AEI/UE - CNS Marques, Ricardo, "Técnicas Avanzadas de Montecarlo Bayesiano para el Renderizado Foto Realista (A-BMC)" financiado por MICIU/AEI/ / y por la Unión Europea "NextGenerationEU"/PRTR, proyecto que finaliza el 31/12/2025.
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