  In this paper, we introduce a new method for classifying 3D objects. Our main
idea is to project a 3D object onto a spherical domain centered around its
barycenter and develop neural network to classify the spherical projection. We
introduce two complementary projections. The first captures depth variations of
a 3D object, and the second captures contour-information viewed from different
angles. Spherical projections combine key advantages of two main-stream 3D
classification methods: image-based and 3D-based. Specifically, spherical
projections are locally planar, allowing us to use massive image datasets (e.g,
ImageNet) for pre-training. Also spherical projections are similar to
voxel-based methods, as they encode complete information of a 3D object in a
single neural network capturing dependencies across different views. Our novel
network design can fully utilize these advantages. Experimental results on
ModelNet40 and ShapeNetCore show that our method is superior to prior methods.
