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A Survey of Temporal Antialiasing Techniques
Author(s) -
Yang Lei,
Liu Shiqiu,
Salvi Marco
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14018
Subject(s) - computer science , upsampling , popularity , variety (cybernetics) , data science , artificial intelligence , image (mathematics) , psychology , social psychology
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.