SparseConvNet is BSD licensed, as found in the LICENSE file.Terms of use.Privacy Links Citations If you find this code useful in your research then please cite: 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks, CVPR 2018 ...
SparseConvNet supports input with different numbers of spatial/temporal dimensions. Higher dimensional input is more likely to be sparse because of the 'curse of dimensionality'. DimensionName in 'torch.nn'Use cases 1Conv1dText, audio 2Conv2dLines in 2D space, e.g. handwriting ...
SparseConvNets既可以通过定义一个继承自torch.nn.Module的函数来构建,也可以通过将模块堆叠在一个稀疏的通道中来实现。序列: import torch import sparseconvnet as scn #Usethe GPUifthereisone, otherwise CPU use_gpu = torch.cuda.is_available() model = scn.Sequential().add( scn.SparseVggNet(2,1, ...
SparseConvNets既可以通过定义一个继承自torch.nn.Module的函数来构建,也可以通过将模块堆叠在一个稀疏的通道中来实现。序列: import torch import sparseconvnet as scn #Usethe GPUifthereisone, otherwise CPU use_gpu = torch.cuda.is_available() model = scn.Sequential().add( scn.SparseVggNet(2,1, ...
【空间稀疏卷积神经络SparseConvNet】“SparseConvNet - A spatially-sparse convolutional neural network” by Ben Graham http://t.cn/RlnU7yd
cross-modal GT-Sampling to balance near-distant virtual objects while ensuring the consistency of point clouds and images.Extensive experiments on the popular KITTI and nuScenes datasets validate the superiority of PVConvNet, with competitive performance compared to state-of-the-art 3D detection ...
This paper proposes a novel two-stream inflated 3D ConvNet based on the sparse regularization (SRI3D) model for action recognition. In order to allow the network to learn the sparsity of output, the ...
SparseConvNet is BSD licensed, as found in the LICENSE file.Terms of use.Privacy Citations If you find this code useful in your research then please cite: 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks, CVPR 2018
Submanifold sparse convolutional networks. Contribute to LeftThink/SparseConvNet development by creating an account on GitHub.
SparseConvNet supports input with different numbers of spatial/temporal dimensions. Higher dimensional input is more likely to be sparse because of the 'curse of dimensionality'. DimensionName in 'torch.nn'Use cases 1 Conv1d Text, audio 2 Conv2d Lines in 2D space, e.g. handwriting 3 Conv3d...