Unsupervised monocular depth estimation with omnidirectional camera for 3D reconstruction of grape berries in the wild.

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Japanese table grapes are quite expensive because their production is highly labor-intensive. In particular, grape berry pruning is a labor-intensive task performed to produce grapes with desirable characteristics. Because it is considered difficult to master, it is desirable to assist new entrants by using information technology to show the recommended berries to cut. In this research, we aim to build a system that identifies which grape berries should be removed during the pruning process. To realize this, the 3D positions of individual grape berries need to be estimated. Our environmental restriction is that bunches hang from trellises at a height of about 1.6 meters in the grape orchards outside. It is hard to use depth sensors in such circumstances, and using an omnidirectional camera with a wide field of view is desired for the convenience of shooting videos. Obtaining 3D information of grape berries from videos is challenging because they have textureless surfaces, highly symmetric shapes, and crowded arrangements. For these reasons, it is hard to use conventional 3D reconstruction methods, which rely on matching local unique features. To satisfy the practical constraints of this task, we extend a deep learning-based unsupervised monocular depth estimation method to an omnidirectional camera and propose using it. Our experiments demonstrate the effectiveness of the proposed method for estimating the 3D positions of grape berries in the wild.