Introduction

An unofficial PyTorch Implementation of PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space[NIPS 2017].

Requirements

PyTorch, Python3, TensorboardX, tqdm, fire

Classification

Start

Dataset: ModelNet40, download it from Official Site or Baidu Disk with hi1i.

Train

python train_clss.py --data_root your_data_root --log_dir your_log_dir

eg.
python train_clss.py --data_root /root/modelnet40_normal_resampled --log_dir cls_ssg_1024

Evaluate

python evaluate.py evaluate_cls model data_root checkpoint npoints

eg.
python evaluate.py evaluate_cls pointnet2_cls_ssg  /root/modelnet40_normal_resampled \
checkpoints/pointnet2_cls_250.pth 1024

python evaluate.py evaluate_cls pointnet2_cls_msg root/modelnet40_normal_resampled \
checkpoints/pointnet2_cls_250.pth 1024

Performance(the first row is the results reported in Paper, the following rows are results reported from this repo.)

| Model | NPoints | Aug | Accuracy(%) | | :---: | :---: | :---: | :---: | | PointNet2(official) | 5000 | ✓ | 91.7 | | PointNet2_SSG | 1024 | ✗ | 91.8 | | PointNet2_SSG | 4096 | ✗ | 91.7 | | PointNet2_SSG | 4096 | ✓ | 90.5 | | PointNet2_MSG | 4096 | ✓ | 91.0 |

| Model | Train_NPoints | DP | Test_NPoints | Accuracy(%) | | :---: | :---: | :---: | :---: | :---: | | PointNet2_SSG | 1024 | ✗ | 256 | 67.9 | | PointNet2_SSG | 1024 | ✓ | 256 | 90.8 | | PointNet2_SSG | 1024 | ✗ | 1024 | 91.8 | | PointNet2_SSG | 1024 | ✓ | 1204 | 91.9 |

Train Your own Dataset

Prepare the dataset(n classes) in the ModelNet40 structure

CustomData(dir)
    |- class1(dir)
        | - class1_name11.txt
        | - class1_name12.txt
        ...
    |- class2(dir)
        | - class2_name21.txt
        | - class2_name22.txt
        ...
    |- classn(dir)
    |- shape_names.txt
        | - class1(line1)
        | - class2(line2)
        | - ...
        | - classn(linen)
    |- train.txt
        | - class1_name11
        | - class2_name21
        | - class2_name22
        | - ...
        | - classn_namen1
    |- test.txt
        | - class1_name12
        | - class2_name22
        | - ...
        | - classn_namen2

Start to train

python train_custom_cls.py --data_root your_datapath/CustomData --nclasses 2 --npoints 2048

Start to evaluate

python evaluate_custom.py evaluate_cls pointnet2_cls_ssg your_datapath/CustomData work_dirs/checkpoints/pointnet2_cls_250.pth 2

Part Segmentation

Start

Dataset: ShapeNet part, download it from Official Site or Baidu Disk with 3e5z.

Train

python train_part_seg.py --data_root your_data_root --log_dir your_log_dir

eg.
python train_part_seg.py --data_root /root/shapenetcore_partanno_segmentation_benchmark_v0_normal \
--log_dir seg_ssg --batch_size 64

Evaluate

python evaluate.py evaluate_seg data_root checkpoint

eg.
python evaluate.py evaluate_seg /root/shapenetcore_partanno_segmentation_benchmark_v0_normal \
seg_ssg/checkpoints/pointnet2_cls_250.pth

Metrics: Average IoU

| Model | Metrics | mean | aero | bag | cap | car | chair | ear phone | guitar | knife | lamp | laptop | motor | mug | pistol | rocket | skate board | table | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | PointNet2(official) | IoU | 85.1 | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6 | | PointNet2_SSG | IoU | 84.1 | 82.3 | 75.0 | 80.1 | 77.8 | 90.2 | 73.7 | 90.7 | 84.1 | 82.9 | 95.0 | 69.3 | 93.3 | 80.3 | 55.6 | 76.3 | 80.7 | | PointNet2_SSG | Accuracy | 93.2 | 89.9 | 89.0 | 85.5 | 91.8 | 94.4 | 93.5 | 96.1 | 91.1 | 89.2 | 96.9 | 87.4 | 96.4 | 93.7 | 77.2 | 95.9 | 94.8 |

Pointnet2.PyTorch

Install / Use

README

Introduction

Requirements

Classification

Part Segmentation

Reference