36 research outputs found
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a
transformed LiDAR point cloud as input and directly outputs a point-wise label
map, which is then refined by a conditional random field (CRF) implemented as a
recurrent layer. Instance-level labels are then obtained by conventional
clustering algorithms. Our CNN model is trained on LiDAR point clouds from the
KITTI dataset, and our point-wise segmentation labels are derived from 3D
bounding boxes from KITTI. To obtain extra training data, we built a LiDAR
simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize
large amounts of realistic training data. Our experiments show that SqueezeSeg
achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per
frame), highly desirable for autonomous driving applications. Furthermore,
additionally training on synthesized data boosts validation accuracy on
real-world data. Our source code and synthesized data will be open-sourced
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
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