PointNeXt

Official PyTorch implementation of PointNeXt, for the following paper:

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

by Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Hammoud, Mohamed Elhoseiny, Bernard Ghanem

TL;DR: We propose improved training and model scaling strategies to boost PointNet++ to state-of-the-art level. PointNet++ with the proposed model scaling is named as PointNeXt, the next version of PointNets.

News

📌 Houwen Peng is hiring research interns at Microsoft Research Asia. Contact: houwen.peng@microsoft.com
📌 Bernard Ghanem is hiring visiting students. Monthly salary is paid with free housing. Contact Guocheng if interested: guocheng.qian@kaust.edu.sa
2022/06/12: Code released

Features

In the PointNeXt project, we propose a new and flexible codebase for point-based methods, namely OpenPoints. The biggest difference between OpenPoints and other libraries is that we focus more on reproducibility and fair benchmarking.

Extensibility: supports many representative networks for point cloud understanding, such as PointNet, DGCNN, DeepGCN, PointNet++, ASSANet, PointMLP, and our PointNeXt. More networks can be built easily based on our framework since OpenPoints support a wide range of basic operations including graph convolutions, self-attention, farthest point sampling, ball query, e.t.c.
Reproducibility: all implemented models are trained on various tasks at least three times. Mean±std is provided in the PointNeXt paper. Pretrained models and logs are available.
Fair Benchmarking: in PointNeXt, we find a large part of performance gain is due to the training strategies. In OpenPoints, all models are trained with the improved training strategies and all achieve much higher accuracy than the original reported value.

Ease of Use: Build model, optimizer, scheduler, loss function, and data loader easily from cfg. Train and validate different models on various tasks by simply changing the cfg\*\*.yaml file.

model = build_model_from_cfg(cfg.model)
criterion = build_criterion_from_cfg(cfg.criterion)

Here is an example of pointnet.yaml (model configuration for PointNet model):

model:
  NAME: BaseCls
  encoder_args:
    NAME: PointNetEncoder
    in_channels: 4
  cls_args:
    NAME: ClsHead
    num_classes: 15
    in_channels: 1024
    mlps: [512,256]
    norm_args: 
      norm: 'bn1d'

Online logging: Support wandb for checking your results anytime anywhere.

Installation

git clone git@github.com:guochengqian/PointNeXt.git
cd PointNeXt
source install.sh

Note:

the install.sh requires CUDA 11.1; if another version of CUDA is used, install.sh has to be modified accordingly; check your CUDA version by: nvcc --version before using the bash file;
you might need to read the install.rst for a step-by-step installation if the bash file (install.sh) does not work for you by any chance;
for all experiments, we use wandb for online logging by default. Run wandb --login only at the first time in a new machine, or set wandn.use_wandb=False if you do not want to use wandb. Read the official wandb documentation if needed.

Usage

Check README.md file under cfgs directory for detailed training and evaluation on each benchmark.

For example,

Train and validate on ScanObjectNN for 3D object classification, check cfgs/scanobjectnn/README.md
Train and validate on S3DIS for 3D segmentation, check cfgs/s3dis/README.md

Note:

We use yaml to support training and validation using different models on different datasets. Just use .yaml file accordingly. For example, train on ScanObjectNN using PointNeXt: CUDA_VISIBLE_DEVICES=1 bash script/main_classification.sh cfgs/scanobjectnn/pointnext-s.yaml, train on S3DIS using ASSANet-L: CUDA_VISIBLE_DEVICES=1 bash script/main_segmentation.sh cfgs/s3dis/assanet-l.yaml.
Check the default arguments of each .yaml file. You can overwrite them simply through the command line. E.g. overwrite the batch size, just appending batch_size=32 or --batch_size 32.

Model Zoo

We provide the training logs & pretrained models in column our released trained with the improved training strategies proposed by our PointNeXt through Google Drive.

TP: Throughput (instance per second) measured using an NVIDIA Tesla V100 32GB GPU and a 32 core Intel Xeon @ 2.80GHz CPU.

ScanObjectNN (Hardest variant) Classification

Throughput is measured with 128 x 1024 points.

name	OA/mAcc (Original)	OA/mAcc (our released)	#params	FLOPs	Throughput (ins./sec.)
PointNet	68.2 / 63.4	75.2 / 71.4	3.5M	1.0G	4212
DGCNN	78.1 / 73.6	86.1 / 84.3	1.8M	4.8G	402
PointMLP	85.4±1.3 / 83.9±1.5	87.7 / 86.4	13.2M	31.4G	191
PointNet++	77.9 / 75.4	86.2 / 84.4	1.5M	1.7G	1872
PointNeXt-S	87.7±0.4 / 85.8±0.6	88.20 / 86.84	1.4M	1.64G	2040

S3IDS (6-fold) Segmentation

Throughput (TP) is measured with 16 x 15000 points.

name	mIoU/OA/mAcc (Original)	mIoU/OA/mAcc (our released)	#params	FLOPs	TP
PointNet++	54.5 / 81.0 / 67.1	68.1 / 87.6 / 78.4	1.0M	7.2G	186
PointNeXt-S	68.0 / 87.4 / 77.3	68.0 / 87.4 / 77.3	0.8M	3.6G	227
PointNeXt-B	71.5 / 88.8 / 80.2	71.5 / 88.8 / 80.2	3.8M	8.8G	158
PointNeXt-L	73.9 / 89.8 / 82.2	73.9 / 89.8 / 82.2	7.1M	15.2G	115
PointNeXt-XL	74.9 / 90.3 / 83.0	74.9 / 90.3 / 83.0	41.6M	84.8G	46

S3DIS (Area 5) Segmentation

Throughput (TP) is measured with 16 x 15000 points.

name	mIoU/OA/mAcc (Original)	mIoU/OA/mAcc (our released)	#params	FLOPs	TP
PointNet++	53.5 / 83.0 / -	63.6 / 88.3 / 70.2	1.0M	7.2G	186
ASSANet	63.0 / - /-	65.8 / 88.9 / 72.2	2.4M	2.5G	228
ASSANet-L	66.8 / - / -	68.0 / 89.7/ 74.3	115.6M	36.2G	81
PointNeXt-S	63.4±0.8 / 87.9±0.3 / 70.0±0.7	64.2 / 88.2 / 70.7	0.8M	3.6G	227
PointNeXt-B	67.3±0.2 / 89.4±0.1 / 73.7±0.6	67.5 / 89.4 / 73.9	3.8M	8.8G	158
PointNeXt-L	69.0±0.5 / 90.0±0.1 / 75.3±0.8	69.3 / 90.1 / 75.7	7.1M	15.2G	115
PointNeXt-XL	70.5±0.3 / 90.6±0.2 / 76.8±0.7	71.1 / 91.0 / 77.2	41.6M	84.8G	46

ShapeNetpart Part Segmentation

The code and models of ShapeNetPart will come soon.

ModelNet40 Classificaiton

name	OA/mAcc (Original)	OA/mAcc (our released)	#params	FLOPs	Throughput (ins./sec.)
PointNet++	91.9 / -	93.0 / 90.7	1.5M	1.7G	1872
PointNeXt-S (C=64)	93.7±0.3 / 90.9±0.5	94.0 / 91.1	4.5M	6.5G	2033

Visualization

More examples are available in the paper.

Acknowledgment

This library is inspired by PyTorch-image-models and mmcv.

Citation

If you find PointNeXt or the OpenPoints codebase is useful, please cite:

@Article{qian2022pointnext,
  author  = {Qian, Guocheng and Li, Yuchen and Peng, Houwen and Mai, Jinjie and Hammoud, Hasan and Elhoseiny, Mohamed and Ghanem, Bernard},
  journal = {arXiv:2206.04670},
  title   = {PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies},
  year    = {2022},
}

hadilou/PointNeXt