STSSNet-AAAI2024

## Description
Dataset for "Low-latency Space-time Supersampling for Real-time Rendering"

## Abstract
With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate. Existing techniques often suffer from quality and latency issues due to the disjointed treatment of frame supersampling and extrapolation. In this paper, we recognize the shared context and mechanisms between frame supersampling and extrapolation, and present a novel framework, Space-time Supersampling (STSS). By integrating them into a unified framework, STSS can improve the overall quality with lower latency. To implement an efficient architecture, we treat the aliasing and warping holes unified as reshading regions and put forth two key components to compensate the regions, namely Random Reshading Masking (RRM) and Efficient Reshading Module (ERM). Extensive experiments demonstrate that our approach achieves superior visual fidelity compared to state-of-the-art (SOTA) methods. Notably, the performance is achieved within only 4ms, saving up to 75\% of time against the conventional two-stage pipeline that necessitates 17ms.

## 数据集说明
本数据集对应论文《面向实时渲染的低延迟时空超采样》（Low-latency Space-time Supersampling for Real-time Rendering）

## 摘要
随着实时渲染技术的兴起与显示设备的迭代升级，业界对能够以高帧率输出高分辨率内容的后处理方法的需求日益增长。现有技术往往对帧超采样与帧外推采用割裂式处理方式，因而同时存在画质与延迟方面的缺陷。本文中，我们意识到帧超采样与帧外推之间存在共通的上下文与机制，并提出了一种全新的框架——时空超采样（Space-time Supersampling, STSS）。通过将二者整合至统一框架中，STSS能够在降低延迟的同时提升整体画质表现。为构建高效架构，我们将锯齿走样与扭曲孔洞统一视作重着色区域，并提出了两个用于补偿该区域的核心组件：随机重着色掩码（Random Reshading Masking, RRM）与高效重着色模块（Efficient Reshading Module, ERM）。大量实验结果表明，相较于当前最优（state-of-the-art, SOTA）方法，本文所提方法可实现更优异的视觉保真度。值得注意的是，本方法的运行耗时仅为4毫秒，相较于需耗时17毫秒的传统两阶段流水线，最多可节省75%的运算时间。