  This paper proposes a multi-view extension of instance segmentation without
relying on texture or shape descriptor matching. Multi-view instance
segmentation becomes challenging for scenes with repetitive textures and
shapes, e.g., plant leaves, due to the difficulty of multi-view matching using
texture or shape descriptors. To this end, we propose a multi-view region
matching method based on epipolar geometry, which does not rely on any feature
descriptors. We further show that the epipolar region matching can be easily
integrated into instance segmentation and effective for instance-wise 3D
reconstruction. Experiments demonstrate the improved accuracy of multi-view
instance matching and the 3D reconstruction compared to the baseline methods.
