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.
翻译:本文建议不依赖纹理或形状描述符匹配的多视图区划延伸。 多视图区划对于具有重复纹理和形状(例如植物叶)的场景具有挑战性,因为难以使用纹理或形状描述符进行多视图比对。为此,我们建议采用多视图区域比对方法,以不依赖任何特征描述符的表面极地测量为基础,不依赖任何特征描述符。我们进一步表明,上极区域比对可以很容易地融入实例区划和有效,例如三维重建。实验表明,与基线方法相比,多视图比对和三维重建的准确性有所提高。