https://github.com/Cavendish518/SALT
Awesome semi-automatic labeling tool for general LiDAR point clouds!
https://github.com/Cavendish518/SALT
annotation-tool lidar-panopitc-segmentation lidar-point-cloud
Last synced: 2 months ago
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Awesome semi-automatic labeling tool for general LiDAR point clouds!
- Host: GitHub
- URL: https://github.com/Cavendish518/SALT
- Owner: Cavendish518
- Created: 2025-03-21T07:50:55.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-28T09:36:31.000Z (2 months ago)
- Last Synced: 2025-03-28T10:22:56.350Z (2 months ago)
- Topics: annotation-tool, lidar-panopitc-segmentation, lidar-point-cloud
- Language: C++
- Homepage:
- Size: 54.8 MB
- Stars: 20
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistency
This repo is the official project repository of **[\[SALT\]](https://arxiv.org/abs/2503.23980)**.
## 1. Overview
We introduce SALT, a flexible semi-automatic labeling tool for general LiDAR point clouds, featuring adaptive cross-scene and 4D consistency.
SALT demonstrates exceptional zero-shot adaptability across various sensors, scenes, and motion conditions, greatly enhancing annotation efficiency.
## 2. Environment
The dependencies can be installed from the package manager (tested On Ubuntu 18.04):
```
sudo apt-get install g++ build-essential libeigen3-dev python3-pip \
python3-dev cmake git libboost-all-dev qtbase5-dev libglew-dev libyaml-cpp-dev -y
```
Then, ensure [Anaconda](https://www.anaconda.com/download/) is installed in your system. Create and activate a virtual environment named `SALT`.
```
conda create -n SALT python==3.10
conda activate SALT
```
`SALT` depends on [Patchwork++](https://github.com/url-kaist/patchwork-plusplus), a real-time ground segmentation library. Install it by running:
```
cd 3rdparty
git clone https://github.com/url-kaist/patchwork-plusplus.git && cd patchwork-plusplus
make pyinstall
cd ..
```
[SAM 2](https://github.com/facebookresearch/sam2) is required for the segmentation module. The code requires `torch>=2.5.1` and `torchvision>=0.20.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:
```
cd sam2
pip install -e .
cd checkpoints && ./download_ckpts.sh
cd ../../..
```
or individually download `checkpoint` from [SAM 2](https://github.com/facebookresearch/sam2).
Install `SALT` dependencies and download `Data Alignment Model` checkpoints.
```
pip install -r requirements.txt
cd ..
```
Meanwhile, please manually download the [Data_Alignment_Model](https://drive.google.com/file/d/1pnnW2JQsc8syDMyQiXGdBtSIjWlzfVjD/view?usp=sharing) and place it in the `~/SALT/SALT/` directory.
The project folder structure should look like this:
SALT folder
├── 3rdparty
├── SALT
├── best_model.pth -- Data_Alignment_Model
├── ...
├── src
└── ...
Install system-level dependencies and build the C++ backend of the labeler tool:
```
mkdir build && cd build
cmake ..
make -j5
```
Now the project root directory (e.g. `~/SALT`) should contain a `bin` directory containing the labeler.In the bin directory, just run `./SALT` to start the labeling tool.
## 3. User Manual
### 3.1 Automatic Segmentation of Whole sequence
After loading the raw point cloud sequence data, the user
only needs to click the "SALT" button once to obtain the pre-segmentation results
for the entire sequence. After clicking the "SALT" button, users can modify the config file according to their own data characteristics.
Once the progress bar (zero-shot segmentation step) is complete, the pre-segmentation
results are automatically saved for subsequent semantic and instance labeling. The pre-segmentation results are also
automatically displayed in the user interface with different colors.
### 3.2 Manual Assignment and Refinement for Semantic Annotation
The user is free to define as many semantic classes appear in the
sequence. Users can assign custom semantic labels to the pre-segmentation results based
on their needs. By simply clicking on a predefined color button
representing a specific semantic category and then selecting a point cloud with a particular ID, all
points with that ID will be assigned the chosen label and updated to the corresponding color. This
operation is as intuitive and effortless as a coloring game. Please note that the colors used to display
the pre-segmentation results are designed to avoid overlapping with user-defined semantic label colors.
If users are not satisfied with the pre-annotated results, they can modify them using the polygon tool.
Inherited from LABELER, SALT supports the option to hide other classes, making manual annotation adjustments
more convenient.
### 3.3 Auto Ordering and Manual Refinement for Instance Annotation
Once users are satisfied with the semantic labeling results, they can simply click the "Auto Instance" button
to automatically assign instance IDs to all semantic categories. Users
can then further refine the results by splitting or merging instance IDs within each category.
## 4. Quick Demo
## 5. Acknowledgements
We would like to thank all the pioneers [SemanticKITTI_LABLER](https://github.com/jbehley/point_labeler), [SAM2](https://github.com/facebookresearch/sam2).
## 6. Citation
If your like our projects, please cite us and give this repo a star.