Geometric robust descriptor for 3D point cloud

We propose rotation robust and density robust local geometric descriptor. Local geometric feature of point cloud is used in many applications, for example, to find correspondences in 3D registration and to segment local regions. Usually, application accuracy depends on the discriminative power of the local geometric features. However, there are some problems such as point sparsity, rotated point cloud, and so on. In this paper, we present new local feature generation method to make a rotation robust and density robust descriptor. First, we place kernels aligned around each point and align them to the normal of the point. To avoid the sign problem of the normal vector, we use symmetric kernel point distribution with respect to the tangent plane. Next, from each kernel point, we estimate geometric information which is rotation robust and discriminative. Finally, we operate convolution process with consideration of kernel point structure, and aggregate all kernel features. We experiment our local descriptors on the ModelNet40 dataset [1] for registration and classification, and the ShapeNet part dataset [2] for segmentation. Our descriptor shows discriminative power regardless of point distribution.

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