A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
This repo is a PyTorch implementation for Dynamic Graph CNN for Learning on Point Clouds (DGCNN)(https://arxiv.xilesou.top/pdf/1801.07829). Our code skeleton is borrowed from WangYueFt/dgcnn.
Note that the network structure (Fig. 3) for classification in DGCNN paper is not consistent with the corresponding description in section 4.1 of the paper. The author of DGCNN adopts the setting of classification network in section 4.1, not Fig. 3. We fixed this mistake in Fig. 3 using PS and present the revised figure below.
Tip: The result of point cloud experiment usually faces greater randomness than 2D image. We suggest you run your experiment more than one time and select the best result.
python main_cls.py --exp_name=cls_1024 --num_points=1024 --k=20
python main_cls.py --exp_name=cls_2048 --num_points=2048 --k=40
python main_cls.py --exp_name=cls_1024_eval --num_points=1024 --k=20 --eval=True --model_path=checkpoints/cls_1024/models/model.t7
python main_cls.py --exp_name=cls_2048_eval --num_points=2048 --k=40 --eval=True --model_path=checkpoints/cls_2048/models/model.t7
python main_cls.py --exp_name=cls_1024_eval --num_points=1024 --k=20 --eval=True --model_path=pretrained/model.cls.1024.t7
python main_cls.py --exp_name=cls_2048_eval --num_points=2048 --k=40 --eval=True --model_path=pretrained/model.cls.2048.t7
ModelNet40 dataset
| | Mean Class Acc | Overall Acc | | :---: | :---: | :---: | | Paper (1024 points) | 90.2 | 92.9 | | This repo (1024 points) | 90.9 | 93.3 | | Paper (2048 points) | 90.7 | 93.5 | | This repo (2048 points) | 91.2 | 93.6 |
python main_partseg.py --exp_name=partseg
python main_partseg.py --exp_name=partseg_airplane --class_choice=airplane
python main_partseg.py --exp_name=partseg_eval --eval=True --model_path=checkpoints/partseg/models/model.t7
python main_partseg.py --exp_name=partseg_airplane_eval --class_choice=airplane --eval=True --model_path=checkpoints/partseg_airplane/models/model.t7
python main_partseg.py --exp_name=partseg_eval --eval=True --model_path=pretrained/model.partseg.t7
python main_partseg.py --exp_name=partseg_airplane_eval --class_choice=airplane --eval=True --model_path=pretrained/model.partseg.airplane.t7
ShapeNet part dataset
| | Mean IoU | Airplane | Bag | Cap | Car | Chair | Earphone | Guitar | Knife | Lamp | Laptop | Motor | Mug | Pistol | Rocket | Skateboard | Table | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Shapes | | 2690 | 76 | 55 | 898 | 3758 | 69 | 787 | 392 | 1547 | 451 | 202 | 184 | 283 | 66 | 152 | 5271 | | Paper | 85.2 | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 | | This repo | 85.2 | 84.5 | 80.3 | 84.7 | 79.8 | 91.1 | 76.8 | 92.0 | 87.3 | 83.8 | 95.7 | 69.6 | 94.3 | 83.7 | 51.5 | 76.1 | 82.8 |
The network structure for this task is slightly different with part segmentation, without spatial transform and categorical vector. The MLP in the end is changed into (512, 256, 13) and only one dropout is used after 256.
You have to download
Stanford3dDataset_v1.2_Aligned_Version.zipmanually from https://goo.gl/forms/4SoGp4KtH1jfRqEj2 and place it under
data/
This task use 6-fold training, such that 6 models are trained leaving 1 of 6 areas as the testing area for each model.
python main_semseg.py --exp_name=semseg_6 --test_area=6
python main_semseg.py --exp_name=semseg_eval_6 --test_area=6 --eval=True --model_root=checkpoints/semseg/models/
python main_semseg.py --exp_name=semseg_eval --test_area=all --eval=True --model_root=checkpoints/semseg/models/
python main_semseg.py --exp_name=semseg_eval_6 --test_area=6 --eval=True --model_root=pretrained/semseg/
python main_semseg.py --exp_name=semseg_eval --test_area=all --eval=True --model_root=pretrained/semseg/
Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) dataset
| | Mean IoU | Overall Acc | | :---: | :---: | :---: | | Paper | 56.1 | 84.1 | | This repo | 59.2 | 85.0 |