3D Object Classification via Spherical Projections
This addresses the problem of 3D object classification for computer vision applications, offering an incremental improvement by combining advantages of existing image-based and 3D-based methods.
The paper tackles 3D object classification by projecting 3D objects onto a spherical domain and using neural networks to classify these projections, achieving superior results on ModelNet40 and ShapeNetCore benchmarks.
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.