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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 # 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].


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 maps nuScenes classes to desired class index in MASS. Then will concate the semantic segmentation label for each LiDAR point. is leveraged for multi-frame lidar densification using the ego pose given by nuScenes. 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


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, 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.


Please refer to for the installation of OpenPCDet. please refer to for the installation of pytorch-geometric

Getting Started

Please refer to to learn more usage about this project.


MASS is released under the Apache 2.0 license.


MASS is an open source project for dense top-view semantic segmentation based on LiDAR input. We would thanks for the great baseline.


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

  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},


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.


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







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