PointStack

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Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling

PointStack 최신버젼 다운로드

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# PointStack

This repository provides the official PyTorch implementation for the following paper:

**Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling**<br>
[Kevin Tirta Wijaya](https://www.ktirta.xyz), [Dong-Hee Paek](http://ave.kaist.ac.kr/bbs/board.php?bo_table=sub1_2&wr_id=5), and [Seung-Hyun Kong](http://ave.kaist.ac.kr/)<br>
[\[**arXiv**\]](https://arxiv.org/abs/2205.09962)
> **Abstract:** *Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud.
Unfortunately, such a process may result in a substantial loss of granular information due to the sampling operation.
Moreover, the widely-used max-pooling feature aggregation may exacerbate the loss since it completely neglects information from non-maximum point features.
Due to the compounded loss of information concerning granularity and non-maximum point features, the resulting high-semantic point features from existing networks could be insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes.
To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP).
The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information.
On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features.
Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features.
Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud.
In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point clouds.
Specifically, PointStack outperforms various existing feature learning networks for shape classification and part segmentation on the ScanObjectNN and ShapeNetPart datasets.*

## Preparations

**To install the requirements**:

```setup
# 1. Create new environment
conda create -n <environment name> python=3.7

# 2. Install PyTorch with corresponding cudatoolkit, for example,
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

# 3. Install other requirements
pip install -r requirements.txt
```

**To prepare the dataset**:

1. Create empty directories for 'modelnet40', 'partnormal', and 'scanobjectnn' inside './data' directory.
2. Download the corresponding dataset for [Modelnet40](https://github.com/AnTao97/PointCloudDatasets), [ShapeNetPart](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip), and [ScanObjectNN](https://hkust-vgd.github.io/scanobjectnn/).
3. Create 'data' and 'experiments' directories on the workspace, and organize as follows,

## Training

To train the network, run this command:

```train
python train.py --cfg_file <path_to_cfg_yaml (str)> --exp_name <experiment_name (str)> --val_steps <eval_every_x_epoch (int)>
```

## Evaluation

To evaluate the network with pre-trained weights, run:

```eval
python test.py --cfg_file <path_to_cfg_yaml (str)> --ckpt <path_to_ckpt_pth (str)>
```

## Results

Our pretrained model achieves the following performances on :

### [3D Point Cloud Shape Classification on ModelNet40](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40)

| Model name         | Overall Accuracy  | Class Mean Accuracy |
| ------------------ |---------------- | -------------- |
| [PointStack](https://drive.google.com/file/d/1Emw8kR48htvPSNZ7e2UjO1CKy8W857s6/view?usp=sharing)   |     93.3         |      89.6%       |

### [3D Point Cloud Shape Classification on ScanObjectNN](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn)

| Model name         | Overall Accuracy  | Class Mean Accuracy |
| ------------------ |---------------- | -------------- |
| [PointStack](https://drive.google.com/file/d/1XTfYSkc0m4GKEhcV0wLAsb8GJ3-hBaiz/view?usp=sharing)   |     87.2%         |      86.2%       |

### [3D Part Segmentation on ShapeNetPart](https://paperswithcode.com/sota/3d-part-segmentation-on-shapenet-part)

| Model name         | Instance mIoU  | 
| ------------------ |---------------- |
| [PointStack](https://drive.google.com/file/d/1Gab0_Cmdc-QFDgdMnWNMCP1NnE4_Uex1/view?usp=sharing)   |     87.2%         |

PointStack

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PointStack

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