Transporting Synthetic-Data Benefits to Real Perception Tasks

Authors

  • Zachary Parker Author
  • George Anderson Author
  • Matthew Russell Author

Abstract

Generated driving scenes are useful for perception only if their addition improves a declared task under an appropriate real-data assessment. Visual plausibility alone cannot establish that benefit. This article develops a synthetic-data transfer experiment motivated by OmniDrive. It separates generation quality, training exposure, and downstream evaluation, with matched budgets and independent real scenes. No detector is trained here. The proposed design identifies which observations would support a transport claim and which favorable intermediate results would remain insufficient to establish real perception utility.

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Published

2026-10-02

Issue

Vol. 2 (2026): Archive issue of 2026-10-02

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Articles

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Copyright (c) 2026 Zachary Parker, George Anderson, Matthew Russell

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This work is licensed under a Creative Commons Attribution 4.0 International License.