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Robust structured-light depth mapping via recursive decomposition of binary codes.

Xiaohua FengLiang Gao
Published in: Optical engineering (Redondo Beach, Calif.) (2019)
Structured-light depth cameras rely on projecting and resolving coded patterns on a three-dimensional scene with high contrast. The front-end optics of such depth cameras impose a fundamental restriction on the depth-sensing range and accuracy: the patterns only remain sharp within the depth of field jointly determined by the camera and projector. We present here a robust method to improve the depth-sensing range and accuracy for a structured-light depth camera without changing the underlying optical design. Moreover, it shows the unique advantage in macrophotography of highly light-scattering objects. We analyze the proposed method theoretically and validate it in experiments.
Keyphrases
  • optical coherence tomography
  • high resolution
  • high speed
  • magnetic resonance
  • magnetic resonance imaging
  • computed tomography
  • convolutional neural network
  • deep learning
  • contrast enhanced