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Jitor Library for Point Cloud Processing

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计图点云库

已经实现的模型

Model Classification Segmentation
PointNet
PointNet ++
PointCNN
DGCNN
PointConv

使用方法

git clone this repo # 将库下载的本地
# 您需要将 ModelNet40 和 ShapeNet 数据集下载到 data_util/data/ 里面
sh train_cls.sh # 点云分类的训练和测试 
sh train_seg.sh # 点云分割的训练和测试 

所依赖的库

Python 3.7
Jittor 
Numpy
sklearn
lmdb
msgpack_numpy
...

实验结果

分类训练效果测试

Model Input overall accuracy
PointNet 1024 xyz 87.2
PointNet ++ 4096 xyz + normal 92.3
PointCNN 1024 xyz 92.6
DGCNN 1024 xyz 92.9
PointConv 1024 xyz + normal 92.4

分类训练时间测试

Model Speed up ratio (Compare with Pytorch)
PointNet 1.22
PointNet ++ 2.72
PointCNN 2.41
DGCNN 1.22
PointConv

分割训练效果测试

Model Input pIoU
PointNet 2048 xyz + cls label 83.5
PointNet ++ 2048 xyz + cls label + normal 85.0
PointCNN 2048 xyz + normal 86.0
DGCNN 2048 xyz + cls label 85.1
PointConv 2048 xyz 85.4

分割训练时间测试

Model Speed up ratio (Compare with Pytorch)
PointNet 1.06
PointNet ++ 1.85
PointCNN None (No pytorch implementation)
DGCNN 1.05
PointConv None (No pytorch implementation)

非常欢迎您使用计图的点云库进行相关的研究,如在使用中有问题,欢迎提交 issus。

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