Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization
This addresses depth accuracy issues in ToF cameras for applications like 3D imaging, though it appears incremental as it builds on spectral estimation theory.
The paper tackled the problem of multi-path interference in time-of-flight imaging, which causes erroneous depth maps, by proposing a sparsity regularized solution that separates interfering components using multiple modulation frequencies, resulting in improved depth profiles.
Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitude-modulated signals. For broad illumination or transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity regularized solution that separates K-interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.