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s3dis
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Pytorch framework for doing deep learning on point clouds.
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Updated
Jul 4, 2020 - Python
DaHaiHuha
commented
Sep 7, 2019
I'd recommend adding the supported or suggested environment configuration for the beginners who want to give the code a try.
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
point-cloud
pytorch
shapenet
kitti
pointnet
pointnet2
frustum-pointnet
frustum-pointnets
s3dis
pvcnn
point-voxel-cnn
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Updated
Jun 25, 2020 - Python
Pytorch implementation of 'Graph Attention Convolution for Point Cloud Segmentation'
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Updated
Apr 3, 2019 - Python
Grid-GCN for Fast and Scalable Point Cloud Learning
computer-vision
point-cloud
classification
self-driving-car
segmentation
3d-vision
scannet
pointnet
3dvision
s3dis
graphconv
graphconvoltution
cvpr2020
grid-gcn
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Updated
Mar 19, 2020 - Python
FPConv: Learning Local Flattening for Point Convolution, CVPR 2020
point-cloud
pytorch
semantic-segmentation
3d-vision
scannet
scene-understanding
s3dis
3d-convolutions
cvpr2020
point-convolution
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Updated
Jun 7, 2020 - Python
PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.
computer-vision
deep-learning
point-cloud
pytorch
segmentation
shapenet
vkitti
self-supervised-learning
s3dis
mortonnet
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Updated
May 29, 2019 - Python
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@QingyongHu i have few queries w r t sampling feature
Q1. As mentioned in the paper you have perfomed random sampling sinc the point cloud density was 10^5 , should we make change in the source code if we have the point cloud density to be 10^3/ 10^4
Q2 can we disable the random sampling few if we have less points in the point cloud is there any option available
Q3 how to set the value of cla