计算机科学
投影(关系代数)
计算机视觉
人工智能
亮度
校准
解算器
可视化
比例(比率)
延迟(音频)
计算机图形学(图像)
算法
光学
物理
电信
量子力学
数学
统计
程序设计语言
作者
Philipp Kurth,Markus Leuschner,Marc Stamminger,Frank Bauer
标识
DOI:10.1109/tvcg.2022.3203085
摘要
Projection mapping with inexpensive hardware often suffers from calibration errors that lead to visually compromised results. In this paper, we classify common errors that lead to typical visual artifacts. Based on this classification, we present the first content-aware brightness solver. It is tailored for high GPU performance, yet efficiently hides the most common calibration artifacts. Moreover, it is specifically designed to handle both single and larger networked projection mapping setups with minimal latency.
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