Awesome
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
denotesPointNet++-like
operators.Pseudo Grid
denotesKPConv-like
operators.Adapt Weights
denotesContinuousConv-like
operators.PosPool
is a new parameter-free operator.PosPool*
denotes the sin/cos embedding variant ofPosPool
(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).