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.

本文提出了一种新颖的端到端深度神经网络模型，用于从广角多视角立体成像中实现全向深度估计，并提出了大规模的合成数据集来用于算法的训练与测试。结果表明，所提出的方法在合成和实际环境中都生成了出色的结果，并优于现有技术。