Example of our ToF reconstruction and comparison on a real scene. The mesh geometry is color-coded according to surface normal (i.e., blue indicates left-faced surface while red the opposite). The naive results are generated by built-in software in ToF cameras. Our method clearly improves the results by reducing flying pixels and blurriness and suppressing the noise in both amplitude and depth.


Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications. However, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. Another shortcoming is the limited spatial resolution of currently available ToF sensors.

In this paper we analyze the image formation model for blurred ToF images. By directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of ToF cameras. Our method outperforms existing methods on both synthetic and real datasets. In the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent ToF cameras.


Paper [DefocusToF_Xiao2015.pdf (10MB)]
Supplementary [DefocusToF_Supplementary_Xiao2015.pdf (0.2MB)]

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