Omnidirectional depth sensing has its advantage over the conventional stereo
systems since it enables us to recognize the objects of interest in all
directions without any blind regions. In this paper, we propose a novel
wide-baseline omnidirectional stereo algorithm which computes the dense depth
estimate from the fisheye images using a deep convolutional neural network. The
capture system consists of multiple cameras mounted on a wide-baseline rig with
ultrawide field of view (FOV) lenses, and we present the calibration algorithm
for the extrinsic parameters based on the bundle adjustment. Instead of
estimating depth maps from multiple sets of rectified images and stitching
them, our approach directly generates one dense omnidirectional depth map with
full 360-degree coverage at the rig global coordinate system. To this end, the
proposed neural network is designed to output the cost volume from the warped
images in the sphere sweeping method, and the final depth map is estimated by
taking the minimum cost indices of the aggregated cost volume by SGM. For
training the deep neural network and testing the entire system, realistic
synthetic urban datasets are rendered using Blender. The experiments using the
synthetic and real-world datasets show that our algorithm outperforms the
conventional depth estimation methods and generate highly accurate depth maps.

本文提出了一种新颖的广基线全向立体算法，使用深度卷积神经网络从鱼眼图像计算密集深度估计，直接生成具有全 360 度覆盖的密集全向深度图，该算法的实验结果表明，在合成和实际数据集上，比传统的深度估计方法具有更高的准确性。