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Convolutional neural network: Difference between revisions
→Spherical Images
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* [https://arxiv.org/abs/1811.08196 SpherePHD (CVPR 2019)] proposes using faces of an icosahedron as pixels. They propose a kernel which considers the neighboring 9 triangles of each triangle. | * [https://arxiv.org/abs/1811.08196 SpherePHD (CVPR 2019)] proposes using faces of an icosahedron as pixels. They propose a kernel which considers the neighboring 9 triangles of each triangle. | ||
* [https://arxiv.org/abs/1807.03247 CoordConv] adds additional channels to each 2D convolution layer which feeds positional information (UV coordinates) to the convolutional kernel. This allows the kernel to account for distortions. Note that the positional information is merely UV coordinates and is not learned like in NLP. | * [https://arxiv.org/abs/1807.03247 CoordConv] adds additional channels to each 2D convolution layer which feeds positional information (UV coordinates) to the convolutional kernel. This allows the kernel to account for distortions. Note that the positional information is merely UV coordinates and is not learned like in NLP. | ||
* [https://arxiv.org/pdf/1901.02039.pdf Jiang et al.] perform convolutions on meshes using linear combination of first order derivatives and the Laplacian second order derivative. These derivatives are estimated based on neighboring vertices and faces. Experiments are performed on a sphere mesh. | * [https://arxiv.org/pdf/1901.02039.pdf Jiang et al.] perform convolutions on meshes using linear combination of first order derivatives and the Laplacian second order derivative. These derivatives are estimated based on the values and positions of neighboring vertices and faces. Experiments are performed on a sphere mesh. | ||