Browsing by Author "Liu, Shiqiu"

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    ReSTIR GI: Path Resampling for Real-Time Path Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Ouyang, Yaobin; Liu, Shiqiu; Kettunen, Markus; Pharr, Matt; Pantaleoni, Jacopo; Binder, Nikolaus and Ritschel, Tobias
    Even with the advent of hardware-accelerated ray tracing in modern GPUs, only a small number of rays can be traced at each pixel in real-time applications. This presents a significant challenge for path tracing, even when augmented with state-of-the art denoising algorithms. While the recently-developed ReSTIR algorithm [BWP*20] enables high-quality renderings of scenes with millions of light sources using just a few shadow rays at each pixel, there remains a need for effective algorithms to sample indirect illumination. We introduce an effective path sampling algorithm for indirect lighting that is suitable to highly parallel GPU architectures. Building on the screen-space spatio-temporal resampling principles of ReSTIR, our approach resamples multi-bounce indirect lighting paths obtained by path tracing. Doing so allows sharing information about important paths that contribute to lighting both across time and pixels in the image. The resulting algorithm achieves a substantial error reduction compared to path tracing: at a single sample per pixel every frame, our algorithm achieves MSE improvements ranging from 9.3x to 166x in our test scenes. In conjunction with a denoiser, it leads to high-quality path traced global illumination at real-time frame rates on modern GPUs.
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    A Survey of Temporal Antialiasing Techniques
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Yang, Lei; Liu, Shiqiu; Salvi, Marco; Mantiuk, Rafal and Sundstedt, Veronica
    Temporal Antialiasing (TAA), formally defined as temporally-amortized supersampling, is the most widely used antialiasing technique in today's real-time renderers and game engines. This survey provides a systematic overview of this technique. We first review the history of TAA, its development path and related work. We then identify the two main sub-components of TAA, sample accumulation and history validation, and discuss algorithmic and implementation options. As temporal upsampling is becoming increasingly relevant to today's game engines, we propose an extension of our TAA formulation to cover a variety of temporal upsampling techniques. Despite the popularity of TAA, there are still significant unresolved technical challenges that affect image quality in many scenarios. We provide an in-depth analysis of these challenges, and review existing techniques for improvements. Finally, we summarize popular algorithms and topics that are closely related to TAA. We believe the rapid advances in those areas may either benefit from or feedback into TAA research and development.
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    Temporally Reliable Motion Vectors for Real-time Ray Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Zeng, Zheng; Liu, Shiqiu; Yang, Jinglei; Wang, Lu; Yan, Ling-Qi; Mitra, Niloy and Viola, Ivan
    Real-time ray tracing (RTRT) is being pervasively applied. The key to RTRT is a reliable denoising scheme that reconstructs clean images from significantly undersampled noisy inputs, usually at 1 sample per pixel as limited by current hardware's computing power. The state of the art reconstruction methods all rely on temporal filtering to find correspondences of current pixels in the previous frame, described using per-pixel screen-space motion vectors. While these approaches are demonstrated powerful, they suffer from a common issue that the temporal information cannot be used when the motion vectors are not valid, i.e. when temporal correspondences are not obviously available or do not exist in theory. We introduce temporally reliable motion vectors that aim at deeper exploration of temporal coherence, especially for the generally-believed difficult applications on shadows, glossy reflections and occlusions, with the key idea to detect and track the cause of each effect. We show that our temporally reliable motion vectors produce significantly better temporal results on a variety of dynamic scenes when compared to the state of the art methods, but with negligible performance overhead.