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MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding

We are aiming at recovering dense top-view semantic segmentation based on sparse LiDAR signal input. Our code is built based on OpenPCDet repo https://github.com/open-mmlab/OpenPCDet.git. # OpenPCDet

OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection.

It is also the official code release of [PointRCNN], [Part-A^2 net] and [PV-RCNN].

Introduction

Label Densification

Nuscenes tools: The tools for generating dense top-view label through multi-scene aggregating and top-view projection is in label processing tools. The lidaeseg_annotools.py maps nuScenes classes to desired class index in MASS. Then lidar_seg_label_cating.py will concate the semantic segmentation label for each LiDAR point. ego.py is leveraged for multi-frame lidar densification using the ego pose given by nuScenes. gt_img.py gives the color map and visualize the prediction/label.

Visibility Map Generation

Code is in voxelize folder, please run cmake first to build it and use dense.cpp to generate the visibility map. Thanks to https://github.com/peiyunh/wysiwyg.git.

Branches

Master branch is currently set up for SemanticKITTI dataset. Config file can be found in tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml, please follow the guidance of OpenPCDet to run the code.

Code for nuScenes dataset can be found in branch develop_nusc.

For SemanticKitti dataset, if you have get accessed to Kitti-odo and SemanticKitti dataset, please forward the access email from KITTI to pengkunyu1013@gmail.com, the download link for the dense and sparse top-view semantic segmentation label will be given.

The label is stored in binary file wuth data type float32 and should be reshaped as (500,1000) to match our code.

semantic_kitti_pillarseg is for SemanticKitti dataset while using the same config file as tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml to run.

Installation

Please refer to INSTALL.md for the installation of OpenPCDet. please refer to https://github.com/pyg-team/pytorch_geometric for the installation of pytorch-geometric

Getting Started

Please refer to GETTING_STARTED.md to learn more usage about this project.

License

MASS is released under the Apache 2.0 license.

Acknowledgement

MASS is an open source project for dense top-view semantic segmentation based on LiDAR input. We would thanks https://github.com/open-mmlab/OpenPCDet.git for the great baseline.

Citation

If you find this project useful in your research, please consider cite:

@article{peng2021mass,
  title={MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding},
  author={Peng, Kunyu and Fei, Juncong and Yang, Kailun and Roitberg, Alina and Zhang, Jiaming and Bieder, Frank and Heidenreich, Philipp and Stiller, Christoph and Stiefelhagen, Rainer},
  journal={arXiv preprint arXiv:2107.00346},
  year={2021}
}

Contribution

Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions.

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MASS: Multi-Attentional Semantic Segmentation ofLiDAR Data for Dense Top-View Understanding

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