A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

Xiangyu Yue, Bichen Wu, Sanjit A. Seshia, Kurt Keutzer, and Alberto L. Sangiovanni-Vincentelli. A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR), pp. 458–464, June 2018.

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Abstract

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed.

BibTeX

@inproceedings{yue-icmr18,
  author    = {Xiangyu Yue and
               Bichen Wu and
               Sanjit A. Seshia and
               Kurt Keutzer and
               Alberto L. Sangiovanni{-}Vincentelli},
  title     = {A {LiDAR} Point Cloud Generator: from a Virtual World to Autonomous Driving},
  booktitle = {Proceedings of the 2018 {ACM} on International Conference on Multimedia
               Retrieval (ICMR)},
  pages     = {458--464},
  year      = {2018},
  month     = {June},
  abstract  = {3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed.},
}