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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… Click to show full abstract
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.
Journal Title: IEEE Transactions on Visualization and Computer Graphics
Year Published: 2022
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