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Convolutional neural network: Difference between revisions
→Spherical Images
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* [http://papers.nips.cc/paper/6656-learning-spherical-convolution-for-fast-features-from-360-imagery Learning Spherical Convolution for Fast Features from 360 Imagery (NIPS 2017)] proposes using different kernels with different weights and sizes for different altitudes \(\phi\). | * [http://papers.nips.cc/paper/6656-learning-spherical-convolution-for-fast-features-from-360-imagery Learning Spherical Convolution for Fast Features from 360 Imagery (NIPS 2017)] proposes using different kernels with different weights and sizes for different altitudes \(\phi\). | ||
* [https://www.tu-chemnitz.de/etit/proaut/publications/schubert19_IV.pdf Circular Convolutional Neural Networks (IV 2019)] proposes padding the left and right sides of each input and feature map using pixels such that the input wraps around. This works since equirectangular images wrap around on the x-axis. | * [https://www.tu-chemnitz.de/etit/proaut/publications/schubert19_IV.pdf Circular Convolutional Neural Networks (IV 2019)] proposes padding the left and right sides of each input and feature map using pixels such that the input wraps around. This works since equirectangular images wrap around on the x-axis. | ||
* [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. They also develop methods for pooling. | ||
* [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 the values and positions of 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. | ||