Lift: An Educational Interactive Stochastic Ray Tracing Framework with AI-Accelerated Denoiser

Abstract

Real-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With the introduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint started to look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTX hardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available by Khronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiser available from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate 3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac- ing and provides the foundations for laboratory assignments and projects development. The platform aims to make easier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren- dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers. The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as the rendering method, to define the number of samples per pixel and the path length as well as to denoise the generated image either every frame or just the final frame. Statistics related with the timings, image resolution and total number of accumulated samples are provided too. Such platform will teach that nowadays physically-accurate images can be rendered in real-time under different lighting conditions and how well a denoiser can reconstruct images rendered with just one sample per pixel.