This document discusses challenges in object classification using LiDAR point cloud data compared to images. Point clouds can be much larger in size than images, ranging from 250-500 MB. While tools like OpenCV are mature for image analysis, PCL tools for point clouds have theoretical underpinnings that are not robust to real-world noisy data. The author's work involves creating noise-robust 3D pattern recognition techniques, dimensionality reduction methods for machine learning on point clouds using trigonometry and calculus, and generative adversarial networks to generate new diverse datasets.

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Akbar Ladak
ladak@kaaenaat.com
Object Classification in 3D:
Working with LiDAR point clouds

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Challenge adapting to PointClouds:Scale
Images
12.4 MB
• Uncompressed RGB 16-bit frame, 2.1 MP, 24 bit
•
Point Clouds
~85 MB
• Point Cloud LAS file with 2.1 m points
• Most files between 250 to 500 MB

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Challenge adapting to PointClouds:Tools
Images
OpenCV
• Mature toolset with
time-tested algorithms
• High fidelity cameras with
relatively low noise
Point Clouds
PCL
• Theoretical Underpinnings unable to adapt
to real world, noisy data
• Needed to create techniques
that would be robust to noisy data

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OurWork (More details to follow)
1. Creating 3D pattern recognition techniques robust to noise
2. Dimensionality reduction for Machine Learning using 3D
trigonometry, calculus & heuristics
3. Generative Adversarial Networks to create new diverse
datasets to extend the applicability of the algorithm