# RandLA-Net-Pytorch-New

**Repository Path**: kai-kai123/RandLA-Net-Pytorch-New

## Basic Information

- **Project Name**: RandLA-Net-Pytorch-New
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2023-10-29
- **Last Updated**: 2025-01-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

This repository contains a PyTorch implementation of [RandLA-Net](http://arxiv.org/abs/1911.11236) on S3DIS and Semantickitti.

**This repository is mainly based on the [repository](https://github.com/qiqihaer/RandLA-Net-pytorch)**

## Preparation(S3DIS for example)

1. Clone this repository
2. Install some Python dependencies, such as scikit-learn. All packages can be installed with pip.
3. env : ubuntu 18.04, python 3.7.16, torch 1.12.1, numpy 1.21.5, torchvision 0.13.1, scikit-learn 0.22.2, pandas 1.3.5, tqdm 4.64.1
4. Install python functions. the functions and the codes are copied from the [official implementation with Tensorflow](https://github.com/QingyongHu/RandLA-Net).

```
sh compile_op.sh
```

5. Attention: please check out *./utils/nearest_neighbors/lib/python/KNN_NanoFLANN-0.0.0-py3.7-linux-x86_64.egg/* and copy the **.so** file to the parent folder **(update in 2023.2.23: We provide a **.so** file for python3.7, and you don't need to copy(even don't need to compile the cpp code) if you are using python3.7)**
6. Download the Stanford3dDataset_v1.2_Aligned_Version[ dataset](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1), and preprocess the data:

```
  python utils/data_prepare_s3dis.py
```

   Note: Please change the dataset path in the 'data_prepare_s3dis.py' with your own path.

## Train a model(S3DIS for example)

```
  python main_S3DIS.py
```

## Test a model(S3DIS for example)

```
  python test_S3DIS.py
```

## Results

### S3DIS

We train this network for 100 epoches, and the eval results(after voting) in the Area 5 are as follows: mIoU = 62.59%

```
--------------------------------------------------------------------------------------
62.59 | 91.92 96.32 81.43  0.00 20.59 61.54 55.26 75.03 84.95 56.12 72.33 65.93 52.29 
--------------------------------------------------------------------------------------
```

while [SQN](https://github.com/QingyongHu/SQN) shows the result(mIoU) of RandLA-Net of Area5 is 63.59.

our results are close to the original paper.

### SemanticKITTI

We train the network for 100 epoches, and the eval results(after voting) in the Seq 08 are as follows: mIoU = 54.62%

```
--------------------------------------------------------------------------------------------------------------------------
54.62 | 93.12 18.31 30.68 79.83 45.59 51.81 70.18  0.00 92.15 41.53 78.42  1.09 87.61 46.32 84.30 58.67 72.12 52.28 33.67 
--------------------------------------------------------------------------------------------------------------------------
```

The checkpoint is in the output folder.