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In computer graphics, there are three primary research areas: modeling, rendering, and animation, which is generally accepted by graphics practitioners. Modeling deals with the specification of shape and appearance in a way that can be represented mathematically and be stored on the computer. Rendering deals with the creation of shaded images, focusing on interactions between lights and 3D geometric models. Animation creates an illusion of motion through the sequence of images.

CGLab at SKKU particularly deals with rendering and its associated areas. Fundamental principles underlying the topics include physics, optics, GPU algorithms, and visual perception. Detailed subjects are listed in what follows.

Real-Time Rendering, GPU Ray Tracing, and Optics Simulation
Despite the strides made in graphics algorithms and hardware, real-time rendering of natural phenomena remains challenging. In general, we sacrifice quality for real-time performance, which approximates physics. However, we believe there are always creative possibilities to improve images as similar in quality to reference solutions, while maintaining interactive real-time performance. GPUs (Graphics processing units) considerably help us to realize our novel algorithms and data structures with high performance.

Our former studies on lens blur effects, lens flare, and optical ray tracing were successful attempts to prove our belief. Creative combinations of rasterization and ray tracing allowed us to achieve real-time performance and high image quality at the same time. We are still seeking for creative solutions to many open rendering problems.

* Associated grants: NRF Korea, 2012M3A6A3055695
Metaverse and VR Display Algorithms
Metaverse is one of the actively ongoing multidisciplinary subject areas. We attempt to investigate technological aspects of Metaverse, including modeling of virtual avatar, virtual environments, display algorithms, visual fatigue, and VR hardware. Many possibilities are open now, and we try to build new techniques on the basis of our rendering and modeling algorithms.

Real-time global illumination for virtual reality (VR) and augmented reality (AR) requires to be computed with hard real-time constraints, usually higher than 60 frames per sec. We are trying to develop efficient techniques to achieve visually plausible and temporally coherent appearances. In particular, volume-based approximation of global illumination techniques are improved. Also, the global illumination techniques for pure VR are extended to AR with efficient acquisition of scene geometry, light sources, and materials from input video streams.

Stereoscopic (binocular) display needs to be employed to mediate interactive VR/AR experiences. Such display devices still incur visual fatigues in many optical and perceptual aspects. To cope with these problems, we are investigating how to improve optical accuracy of VR display in terms of motion blur and optical aberrations.

* Associated grants: NRF Korea (2012M3A6A3055695, 2017M3C1B6070980), ITRC, Samsung Electronics 2016
Deep Learning and Rendering
Deep learning (DL) allows us to explore many open-problem spaces, in which we usually cannot intuitively obtain a computational model. We are investigating its possibilities in the two aspects

Rendering for DL focuses on the generation of (labeled) images that can be fed into the network as input. This significantly helps to widen the application areas of DL, where we cannot easily attain the input data. To this end, we divert realistic rendering towards imperfect real imagery (CG images are too ideal and clean for this purpose).

Another possibility is DL for rendering. Unlike what it sounds like, DL's mechanism and rendering is contradicting, because many phenomena in the rendering are computationally predictable with explicit models. Instead, we now understand DL as a compact nonlinear modeler for many rendering problems, and explore many possibilities in how it can be effectively utilized modeling-based areas.

* Associated grants: Samsung Research Funding & Incubation Center for Future Technology, SRFC-IT1901-01
Very-High-Resolution GPU Imaging
As 4K and 8K displays become popular, the traditional raster algorithm/pipelines may potentially encounter a bottleneck in the pixel processing. Processing in a native resolution may not be optimal in the near future. To this end, we are investigating how to design a novel pipeline with resolution-independent G-buffers, which encodes geometry and shading information in much lower data-space complexity and reconstructs at a higher resolution without precision loss.

* Associated grants: NRF Korea, 2019R1A2C2002449
GPU Algorithms
Rendering usually handles a gigantic amount of data. To facilitate rendering, graphics hardware has been rapidly evolving the recent decades. One of the important advances is a user-programmable rendering pipeline. Accordingly, the capability of GPU expands beyond the traditional usage to encompass general-purpose computing. We attempt to achieve improved performance in general computing up to order of two magnitudes. Such an approach is focused on creative algorithms rather than a simple use of GPU and CUDA/OpenCL.

* Associated grants: NRF Korea (2012R1A2A2A01045719, 2015R1A2A2A01003783)
Perception-Based Visualization
Effective visualization of informative data involves in-depth understandings on human visual perception. Unlike common approaches based on image analysis, our research advances more to image synthesis solutions, leading to perceptually-effective visualization. Visual saliency is one of the important keys to such approaches.

* Associated grants: NRF Korea, 2011-0014015
27336, College of Software, Sungkyunkwan University, Tel. +82 31-299-4917, Seobu-ro 2066, Jangan-gu, Suwon, 16419, South Korea
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