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Self-Supervised Joint Learning of Patterns and Matching for Structured Light Depth Estimation

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Abstract

We present a learned method for structured-light depth estimation, where the projected pattern is jointly optimized alongside the depth-matching network. An in-network Tenderer simulates pattern projection during training. Results are precise, achieving 1/30th-pixel plane RMSE.

© 2020 The Author(s)

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