KAUST DepartmentComputational Imaging Group
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Online Publication Date2018-12-18
Print Publication Date2018-06
Permanent link to this recordhttp://hdl.handle.net/10754/628904
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AbstractWe present an end-to-end image processing framework for time-of-flight (ToF) cameras. Existing ToF image processing pipelines consist of a sequence of operations including modulated exposures, denoising, phase unwrapping and multipath interference correction. While this cascaded modular design offers several benefits, such as closed-form solutions and power-efficient processing, it also suffers from error accumulation and information loss as each module can only observe the output from its direct predecessor, resulting in erroneous depth estimates. We depart from a conventional pipeline model and propose a deep convolutional neural network architecture that recovers scene depth directly from dual-frequency, raw ToF correlation measurements. To train this network, we simulate ToF images for a variety of scenes using a time-resolved renderer, devise depth-specific losses, and apply normalization and augmentation strategies to generalize this model to real captures. We demonstrate that the proposed network can efficiently exploit the spatio-temporal structures of ToF frequency measurements, and validate the performance of the joint multipath removal, denoising and phase unwrapping method on a wide range of challenging scenes.
CitationSu S, Heide F, Wetzstein G, Heidrich W (2018) Deep End-to-End Time-of-Flight Imaging. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Available: http://dx.doi.org/10.1109/CVPR.2018.00668.
Conference/Event name31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018