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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Core Concepts
2. The Nuts and Bolts of Neural Networks FREE CHAPTER 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

Introduction to 3D data processing

The lidar produces a point cloud—a set of data points in a three-dimensional space. Remember that the lidar emits laser beams. A beam reflecting off of a surface and returning to the receiver generates a single data point of the point cloud. If we assume that the lidar device is the center of the coordinate system and each laser beam is a vector, then a point is defined by the vector's direction and magnitude. Therefore, the point cloud is an unordered set of vectors. Alternatively, we can define the points by their Cartesian coordinates in space, as illustrated in the left side of the following diagram. In this case, the point cloud is a set of vectors , where each vector contains the three coordinates of the point. For the sake of clarity, each point is represented as a cube:

Left: Points (represented as cubes) in the 3D space...
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