计算机科学
人工智能
多径干扰
模块化设计
规范化(社会学)
管道(软件)
多径传播
降噪
卷积神经网络
计算机视觉
实时计算
电信
程序设计语言
人类学
社会学
频道(广播)
操作系统
作者
Shuochen Su,Felix Heide,Gordon Wetzstein,Wolfgang Heidrich
标识
DOI:10.1109/cvpr.2018.00668
摘要
We 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.
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