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 pointlevel labels from a computer game. To our best knowledge, this is
the first publication on LiDAR point cloud simulation framework
for autonomous driving. 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 registration 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.
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Point Cloud Segmentation based on Hypergraph Spectral Clustering
Hypergraph spectral analysis has emerged as an
effective tool processing complex data structures in data analysis.
The surface of a three-dimensional (3D) point cloud and the
multilateral relationship among their points can be naturally
captured by the high-dimensional hyperedges. This work investigates
the power of hypergraph spectral analysis in unsupervised
segmentation of 3D point clouds. We estimate and order the
hypergraph spectrum from observed point cloud coordinates.
By trimming the redundancy from the estimated hypergraph
spectral space based on spectral component strengths, we develop
a clustering-based segmentation method. We apply the proposed
method to various point clouds, and analyze their respective
spectral properties. Our experimental results demonstrate the
effectiveness and efficiency of the proposed segmentation method.
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- Award ID(s):
- 1934568
- NSF-PAR ID:
- 10282262
- Date Published:
- Journal Name:
- 2020 Information Theory and Applications Workshop (ITA)
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Automated, ship‐board flow cytometers provide high‐resolution maps of phytoplankton composition over large swaths of the world's oceans. They therefore pave the way for understanding how environmental conditions shape community structure. Identification of community changes along a cruise transect commonly segments the data into distinct regions. However, existing segmentation methods are generally not applicable to flow cytometry data, as these data are recorded as ‘point cloud’ data, with hundreds or thousands of particles measured during each time interval. Moreover, nonparametric segmentation methods that do not rely on prior knowledge of the number of species are desirable to map community shifts.
We present CytoSegmenter, a kernel‐based change‐point estimation method for segmenting point cloud data. Our method allows us to represent and summarize a point cloud of data points by a single element in a Hilbert space. The change‐point locations can be found using a fast dynamic programming algorithm.
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CytoSegmenter is generally applicable for segmenting series of point cloud data from any domain. Moreover, it readily scales to thousands of point clouds, each containing thousands of points. In the context of flow cytometry data collected during research cruises, it does not require prior clustering of particles to define taxa labels, eliminating a potential source of error. This represents an important advance in automating the analysis of large datasets now emerging in biological oceanography and other fields. It also allows for the approach to be applied during research cruises.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ITA50056.2020.9244954
