OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
This work addresses depth estimation for ultra wide field-of-view cameras, which is incremental as it adapts existing methods to omnidirectional data.
The authors tackled omnidirectional depth estimation from wide-baseline multi-view stereo by proposing an end-to-end deep neural network model, which outperformed prior art and state-of-the-art conventional stereo algorithms in synthetic and real-world environments.
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 the concentric spheres swept through all candidate depths using the calibrated camera parameters. The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information. In addition, we present large-scale synthetic datasets for training and testing omnidirectional multi-view stereo algorithms. Our datasets consist of 11K ground-truth depth maps and 45K fisheye images in four orthogonal directions with various objects and environments. Experimental results show that the proposed method generates excellent results in both synthetic and real-world environments, and it outperforms the prior art and the omnidirectional versions of the state-of-the-art conventional stereo algorithms.