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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

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

Awesome

Introduction

Requirements

Classification

Part Segmentation

Reference