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HyperDepth: Learning Depth from Structured Light without Matching
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Structured light sensors are popular due to their robustness to untextured scenes and multipath. ...
We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy. ...
Learning to Recognize Structured Light In this section we reformulate the spatial structured light correspondence problem from a machine learning perspec-tive, and show how disparity maps with subpixel ...
doi:10.1109/cvpr.2016.587
dblp:conf/cvpr/FanelloRTKOKI16
fatcat:semu6akx6jb2veqw2zvpevz5k4
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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compared to time of flight or traditional structured light techniques. ...
We efficiently solve the specialized problem of stereo matching under active illumination using a new learning-based algorithm. ...
[14] use diffuse infrared light to learn a shape from shading mapping from IR intensity to depth, but the technique works only for hands and faces in a very limited range. ...
doi:10.1109/cvpr.2017.692
dblp:conf/cvpr/FanelloVRKTDI17
fatcat:auf6kvbp5nfabi2glqbcovpiva
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. ...
We propose a technique for depth estimation with a monocular structured-light camera, i.e., a calibrated stereo set-up with one camera and one laser projector. ...
[41] exploit ideas from self-supervised learning to train an active stereo network without needing ground-truth depth. ...
doi:10.1109/cvpr.2019.00781
dblp:conf/cvpr/RieglerLDKG19
fatcat:p6gx6ir4b5behegpt55xz3qhwa