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A Closer Look at Local Aggregation Operators in Point Cloud Analysis

By Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong

Updates

  • Oct 9, 2020: release more pytorch models for PartNet and S3DIS.
  • Sep 19, 2020: add shapenetpart segmentation.
  • July 3, 2020: initial release.

Introduction

This repo is the official implementation of "A Closer Look at Local Aggregation Operators in Point Cloud Analysis", which provides clean and the best (to-date) implementations for several representative operators including, Point MLP based (PointNet++-Like), Pseudo Grid based (KPConv-Like) and Adapt Weights (ContinuousConv-Like). It also includes a new family of local aggregation operators without learnable weights, named Position Pooling (PosPool), which is simpler than previous operators but performs similarly well or slightly better. Both PyTorch and TensorFlow implementations are given.

Three datasets are tested, including ModelNet, S3DIS and PartNet. Our implementations all achieve (or are close to) the state-of-the-art accuracy on these benchmarks by proper configurations of each operator type. In particular, one settings achieves 53.8 part category mean IoU on PartNet test set, which outperforms previous best implementations by 7.4 mIoU.

Citation

@article{liu2020closerlook3d,
  title={A Closer Look at Local Aggregation Operators in Point Cloud Analysis},
  author={Liu, Ze and Hu, Han and Cao, Yue and Zhang, Zheng and Tong, Xin},
  journal={ECCV},
  year={2020}
}

Main Results

ModelNet40

Method Acc Tensorflow Model Pytorch Model
Point-wise MLP 92.8 Google / Baidu(wquw) Google / Baidu(fj13)
Pseudo Grid 93.0 Google / Baidu(lvw4) Google / Baidu(gmh5)
Adapt Weights 93.0 Google / Baidu(6zrg) Google / Baidu(bbus)
PosPool 92.9 Google / Baidu(pkzd) Google / Baidu(wuuv)
PosPool* 93.2 Google / Baidu(mjb1) Google / Baidu(qcc6)

S3DIS

Method mIoU Tensorflow Model Pytorch Model
Point-wise MLP 66.2 Google / Baidu(4mhy) Google / Baidu(53as)
Pseudo Grid 65.9 Google / Baidu(06ta) Google / Baidu(8skn)
Adapt Weights 66.5 Google / Baidu(7w43) Google / Baidu(b7zv)
PosPool 66.5 Google / Baidu(gqqe) Google / Baidu(z752)
PosPool* 66.7 Google / Baidu(qtkw) Google / Baidu(r96f)

PartNet

Method mIoU (val) mIoU (test) Tensorflow Model Pytorch Model
Point-wise MLP 48.1 51.2 Google / Baidu(zw15) Google / Baidu(wxff)
Pseudo Grid 50.8 53.0 Google / Baidu(0mtr) Google / Baidu(n6b7)
Adapt Weights 50.1 53.5 Google / Baidu(551l) Google / Baidu(pc22)
PosPool 50.0 53.4 Google / Baidu(rb4x) Google / Baidu(3qv5)
PosPool* 50.6 53.8 Google / Baidu(2ts3) Google / Baidu(czyq)

ShapeNetPart

Method mIoU msIoU Acc Pytorch Model
Point-wise MLP 85.7 84.1 94.5 Google / Baidu(mi2m)
Pseudo Grid 86.0 84.3 94.6 Google / Baidu(wde6)
Adapt Weights 85.9 84.5 94.6 Google / Baidu(dy1k)
PosPool 85.9 84.6 94.6 Google / Baidu(r2tr)
PosPool* 86.2 84.8 94.8 Google / Baidu(27ie)

Notes:

  • Overall accuracy for ModelNet40, mean IoU for S3DIS with Area-5, mean part-category IoU for PartNet are reported.
  • Point-wise MLP denotes PointNet++-like operators.
  • Pseudo Grid denotes KPConv-like operators.
  • Adapt Weights denotes ContinuousConv-like operators.
  • PosPool is a new parameter-free operator.
  • PosPool* denotes the sin/cos embedding variant of PosPool (see description in the paper).

Install

  • For tensorflow users, please refer to README.md for more detailed instructions. Our main experiments are conducted using this code base.
  • For pytorch users, please refer to README.md for more detailed instructions.

Acknowledgements

Our tensorflow codes borrowed a lot from KPCONV.

License

The code is released under MIT License (see LICENSE file for details).