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https://github.com/haibo-qiu/GFNet
[TMLR 2022] Geometric Flow Network for 3D Point Cloud Semantic Segmentation
https://github.com/haibo-qiu/GFNet
deep-learning point-cloud pytorch semantic-segmentation
Last synced: about 2 months ago
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[TMLR 2022] Geometric Flow Network for 3D Point Cloud Semantic Segmentation
- Host: GitHub
- URL: https://github.com/haibo-qiu/GFNet
- Owner: haibo-qiu
- Created: 2022-06-25T05:16:12.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-01-10T00:00:34.000Z (almost 2 years ago)
- Last Synced: 2024-08-01T03:42:52.828Z (5 months ago)
- Topics: deep-learning, point-cloud, pytorch, semantic-segmentation
- Language: Python
- Homepage: https://haibo-qiu.github.io/GFNet/
- Size: 10.8 MB
- Stars: 38
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# GFNet [](https://arxiv.org/abs/2207.02605) [](https://openreview.net/forum?id=LSAAlS7Yts) [](https://haibo-qiu.github.io/GFNet/)
This is the Pytorch implementation of our following paper:
>**[GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation](https://arxiv.org/abs/2207.02605)**
>
*Accepted by [TMLR](https://openreview.net/forum?id=LSAAlS7Yts), 2022*
>
*Haibo Qiu, Baosheng Yu and Dacheng Tao*
> Abstract
>
>Point cloud semantic segmentation from projected views, such as range-view (RV) and bird's-eye-view (BEV), has been intensively investigated. Different views capture different information of point clouds and thus are complementary to each other. However, recent projection-based methods for point cloud semantic segmentation usually utilize a vanilla late fusion strategy for the predictions of different views, failing to explore the complementary information from a geometric perspective during the representation learning. In this paper, we introduce a geometric flow network (GFNet) to explore the geometric correspondence between different views in an align-before-fuse manner. Specifically, we devise a novel geometric flow module (GFM) to bidirectionally align and propagate the complementary information across different views according to geometric relationships under the end-to-end learning scheme. We perform extensive experiments on two widely used benchmark datasets, SemanticKITTI and nuScenes, to demonstrate the effectiveness of our GFNet for project-based point cloud semantic segmentation. Concretely, GFNet not only significantly boosts the performance of each individual view but also achieves state-of-the-art results over all existing projection-based models.
>
Segmentation GIF
(_A gif of segmentation results on [SemanticKITTI](http://semantic-kitti.org) by GFNet_)
Framework
**Table of Contents**
* [Installation](#installation)
* [Data preparation](#data-preparation)
* [Training](#training)
* [Inference](#inference)
* [SemanticKITTI](#semantickitti)
* [nuScenes](#nuscenes)
* [Acknowledgment](#acknowledgment)
* [Citation](#citation)
## Installation
1. Clone this repo:
```bash
git clone https://github.com/haibo-qiu/GFNet.git
```
2. Create a conda env with
```bash
conda env create -f environment.yml
```
Note that we also provide the `Dockerfile` for an alternative setup method.
## Data preparation
1. Download point clouds data from [SemanticKITTI](http://semantic-kitti.org) and [nuScenes](https://www.nuscenes.org/nuscenes#download).
2. For SemanticKITTI, directly unzip all data into `dataset/SemanticKITTI`.
3. For nuScenes, first unzip data to `dataset/nuScenes/full` and then use the following cmd to generate pkl files for both training and testing:
```bash
python dataset/utils_nuscenes/preprocess_nuScenes.py
```
4. Final data folder structure will look like:
```
dataset
└── SemanticKITTI
└── sequences
├── 00
├── ...
└── 21
└── nuScenes
└── full
├── lidarseg
├── smaples
├── v1.0-{mini, test, trainval}
└── ...
└── nuscenes_train.pkl
└── nuscenes_val.pkl
└── nuscenes_trainval.pkl
└── nuscenes_test.pkl
```
## Training
- Please refer to `configs/semantic-kitti.yaml` and `configs/nuscenes.yaml` for dataset specific properties.
- Download the [pretrained resnet model](https://drive.google.com/file/d/1I85xLRwUMIeW_7BvdZ4uZ0Lm4j3zxLT1/view?usp=sharing) to `pretrained/resnet34-333f7ec4.pth`.
- The hyperparams for training are included in `configs/resnet_semantickitti.yaml` and `configs/resnet_nuscenes.yaml`. After modifying corresponding settings to satisfy your purpose, the network can be trained in an end-to-end manner by:
1. `./scripts/start.sh` on SemanticKITTI.
2. `./scripts/start_nuscenes.sh` on nuScenes.
## Inference
### SemanticKITTI
1. Download [gfnet_63.0_semantickitti.pth.tar](https://drive.google.com/file/d/1J7jeSY5hGIHZO3WBdZnZLfdH-plv-81g/view?usp=sharing) into `pretrained/`.
2. Evaluate on SemanticKITTI valid set by:
```bash
./scripts/infer.sh
```
Alternatively, you can use the [official semantic-kitti api](https://github.com/PRBonn/semantic-kitti-api#evaluation) for evaluation.
3. To reproduce the results we submitted to the test server:
1. download [gfnet_submit_semantickitti.pth.tar](https://drive.google.com/file/d/1bdq2_l5Q0tyww7wc3wlyrU09H2tVY5LF/view?usp=sharing) into `pretrained/`,
2. uncomment and run the second cmd in `./scripts/infer.sh`.
3. zip `path_to_results_folder/sequences` for submission.
### nuScenes
1. Download [gfnet_76.8_nuscenes.pth.tar](https://drive.google.com/file/d/1r5SXpToLBdiYdNp7we9Bw-Chmc0x7QPd/view?usp=sharing) into `pretrained/`.
2. Evaluate on nuScenes valid set by:
```bash
./scripts/infer_nuscenes.sh
```
3. To reproduce the results we submitted to the test server:
1. download [gfnet_submit_nuscenes.pth.tar](https://drive.google.com/file/d/16nI5NjZ4wgNRwEC_HjqrVQLFLWXqThzs/view?usp=sharing) into `pretrained/`.
2. uncomment and run the second cmd in `./scripts/infer_nuscenes.sh`.
3. check the valid format of predictions by:
```bash
./dataset/utils_nuscenes/check.sh
```
where `result_path` needs to be modified correspondingly.
4. submit the `dataset/nuScenes/preds.zip` to the test server.
## Acknowledgment
This repo is built based on [lidar-bonnetal](https://github.com/PRBonn/lidar-bonnetal), [PolarSeg](https://github.com/edwardzhou130/PolarSeg) and [kprnet](https://github.com/DeyvidKochanov-TomTom/kprnet). Thanks the contributors of these repos!
## Citation
If you use our code or results in your research, please consider citing with:
```bibtex
@article{qiu2022gfnet,
title={{GFN}et: Geometric Flow Network for 3D Point Cloud Semantic Segmentation},
author={Haibo Qiu and Baosheng Yu and Dacheng Tao},
journal={Transactions on Machine Learning Research},
year={2022},
url={https://openreview.net/forum?id=LSAAlS7Yts},
}
```