High-Resolution Depth Maps Based on TOF-Stereo Fusion
Vineet Gandhi, Jan Cech, Radu Horaud

TL;DR
This paper introduces a novel method for fusing TOF and stereo camera data to produce high-resolution, accurate depth maps suitable for robotic applications, overcoming limitations of individual sensors.
Contribution
A new TOF-stereo fusion algorithm using seed-growing and Bayesian propagation that enhances depth map resolution and accuracy beyond existing sensors.
Findings
Outperforms traditional stereo algorithms in accuracy.
Produces higher resolution depth maps than Kinect.
Potential for real-time processing on a single CPU.
Abstract
The combination of range sensors with color cameras can be very useful for robot navigation, semantic perception, manipulation, and telepresence. Several methods of combining range- and color-data have been investigated and successfully used in various robotic applications. Most of these systems suffer from the problems of noise in the range-data and resolution mismatch between the range sensor and the color cameras, since the resolution of current range sensors is much less than the resolution of color cameras. High-resolution depth maps can be obtained using stereo matching, but this often fails to construct accurate depth maps of weakly/repetitively textured scenes, or if the scene exhibits complex self-occlusions. Range sensors provide coarse depth information regardless of presence/absence of texture. The use of a calibrated system, composed of a time-of-flight (TOF) camera and of…
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