OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
2989113604, pp. 8987-8996, 2019.
In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra wide field-of-view (FOV) cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature maps are warped onto...More
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