This repository contains helper scripts to open, visualize, and process point clouds from the HelixNet dataset. It also contains scripts to process data from Semantic-Kitti [1, 2] in slices smaller than one full sensor rotation.
- Dataset presentation
We introduce HelixNet, a new large-scale and open-access LiDAR dataset intended for the evaluation of real-time semantic segmentation algorithms. In contrast to other large-scale datasets, HelixNet includes fine-grained data about the sensor’s rotation and position, as well as the points’ release time.
- Download
The dataset can be downloaded from Zenodo
- Data loading
This repository contains:
📦HelixNet
┣ 📂configs # hydra config files for both datasets
┣ 📂helixnet # PytorchLightning datamodules for both datasets
┃ ┣ 📂laserscan # .py files loading individual files
┣ 📂misc # helpers for visualisation in CloudCompare
┣ 📂demos # some illustrative notebooks- Visualization and Usage
Each point is associated with the 9 following values: (1-3) Cartesian coordinates in a fixed frame of a reference, (4-6) cylindrical coordinate relative to the sensor at the time of acquisition, (7) intensity, (8) fiber index, and (9) packet output time.
See our notebooks in /demos for examples of data manipulation and several visualization functions for semantic segmentation.
Please open an issue to submit new entries. Mention if the work has already been published and wether the code accessible for reproducibility. We require that at least a preprint is available to add an entry.
We use a 9-classes nomenclature: road (16.4% of all points), other surface (22.0%), building (31.3%), vegetation (8.5%), traffic signs (1.6%), static vehicle (4.9%), moving vehicle (2.1%), pedestrian (0.9%), and artifact (0.05%).
Methods that are real-time (Inf. time < Acq. time) are denoted with ✔️, methods that are not with ❌.
| Model name | #Params (M) | mIoU | Inf. (ms) | Published |
|---|---|---|---|---|
| Helix4D | 1.0 | 78.7 | ✔️ 19 | ✔️ link |
| Cylinder3D* | 55.9 | 75.0 | ❌ 54 | ✔️ link |
| PolarNet* | 13.6 | 72.2 | ❌ 36 | ✔️ link |
| SPVNAS* | 10.8 | 69.9 | ❌ 44 | ✔️ link |
| SalsaNeXt* | 6.7 | 68.2 | ✔️ 10 | ✔️ link |
| Model name | #Params (M) | mIoU | Inf. (ms) | Published |
|---|---|---|---|---|
| Helix4D | 1.0 | 79.4 | ✔️ 45 | ✔️ link |
| Cylinder3D* | 55.9 | 76.6 | ❌ 108 | ✔️ link |
| PolarNet* | 13.6 | 73.6 | ✔️ 49 | ✔️ link |
| SPVNAS* | 10.8 | 73.4 | ✔️ 73 | ✔️ link |
| SalsaNeXt* | 6.7 | 69.4 | ✔️ 23 | ✔️ link |
Models that we re-trained ourselves are denoted with a star (*).
If you use this dataset and/or this API in your work, please cite our paper:
@article{loiseau22online,
title={Online Segmentation of LiDAR Sequences: Dataset and Algorithm.},
author={Romain Loiseau and Mathieu Aubry and Loic Landrieu},
journal={ECCV},
year={2022}
}
- This work was supported by ANR project READY3D ANR-19-CE23-0007.
- The point cloud sequence of HelixNet was acquired during the Stereopolis II project. (N. Paparoditis et al. "Stereopolis II: A multi-purpose and multi-sensor 3D mobile mapping system for street visualisation and 3D metrology." Revue française de photogrammétrie et de télédétection, 2012)
- HelixNet was annotated by FUTURMAP.
- We thank Zenodo for hosting the dataset.
- This repository is highly inspired from https://github.com/PRBonn/semantic-kitti-api.
[1] J. Behley et al., SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences, ICCV, 2016.
[2] A. Geiger et al., Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite, CVPR, 2012.