3D Reconstruction with Deep Learning" by Rocher is a comprehensive guide to the field of 3D reconstruction using deep learning techniques. The book provides an in-depth analysis of the latest advancements in the field and offers practical insights into how these techniques can be applied to real-world problems.
The book is divided into several sections, each of which focuses on a specific aspect of 3D reconstruction using deep learning. The first section provides an overview of the fundamentals of deep learning, including the basics of neural networks and their applications in computer vision.
The second section of the book delves into the various techniques used for 3D reconstruction, including stereo reconstruction, structure from motion, and multi-view stereo. The author explains the mathematical and algorithmic underpinnings of each technique and offers practical examples of how they can be used in real-world applications.
The third section of the book focuses on the use of deep learning techniques for 3D reconstruction. The author discusses the latest advancements in convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures, and explains how they can be used for 3D reconstruction.
The fourth section of the book offers practical insights into the implementation of deep learning techniques for 3D reconstruction. The author provides detailed examples of how to implement these techniques using popular deep learning frameworks such as TensorFlow and PyTorch.
Overall, "3D Reconstruction with Deep Learning" is an excellent resource for anyone interested in the field of 3D reconstruction. The book is well-written, easy to understand, and offers practical insights into how to apply deep learning techniques to real-world problems. Whether you are a researcher, a student, or a practitioner in the field of computer vision, this book is a must-read
