3D Imaging, Analysis and Applications

  • Yonghuai Liu
  • Nick Pears
  • Paul L. Rosin
  • Patrik Huber

Table of contents

  1. Front Matter
    Pages i-xii
  2. Johannes Brünger, Reinhard Koch, Nick Pears, Yonghuai Liu, Paul L. Rosin
    Pages 1-36
  3. 3D Shape Acquisition, Representation and Visualisation

    1. Front Matter
      Pages 37-37
    2. Stephen Se, Nick Pears
      Pages 39-107
    3. Marc-Antoine Drouin, Jean-Angelo Beraldin
      Pages 109-165
    4. Marc-Antoine Drouin, Ismail Hamieh
      Pages 167-214
    5. Marc-Antoine Drouin, Lama Seoud
      Pages 215-264
    6. William A. P. Smith
      Pages 265-316
  4. 3D Shape Analysis and Inference

    1. Front Matter
      Pages 317-317
    2. Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano, Federico Tombari
      Pages 319-352
    3. Umberto Castellani, Adrien Bartoli
      Pages 353-411
    4. Benjamin Bustos, Ivan Sipiran
      Pages 413-461
    5. Hang Dai, Nick Pears, Patrik Huber, William A. P. Smith
      Pages 463-512
    6. Charles Ruizhongtai Qi
      Pages 513-566
  5. 3D Imaging Applications

    1. Front Matter
      Pages 567-567
    2. Nick Pears, Ajmal Mian
      Pages 569-630
    3. Gabriele Guidi, Bernard D. Frischer
      Pages 631-697
    4. Ayan Chaudhury, John L. Barron
      Pages 699-732
  6. Back Matter
    Pages 733-736

About this book


This textbook is designed for postgraduate studies in the field of 3D Computer Vision.  It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops.

Overall, the book covers three main areas:

●      3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation;

●      3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and 

●      3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants.

3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline.



3D Imaging 3D Shape Analysis 3D Shape Processing 3D Shape Modelling 3D Depth Cameras

Editors and affiliations

  • Yonghuai Liu
    • 1
  • Nick Pears
    • 2
  • Paul L. Rosin
    • 3
  • Patrik Huber
    • 4
  1. 1.Department of Computer ScienceEdge Hill UniversityOrmskirkUK
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK
  3. 3.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  4. 4.Department of Computer ScienceUniversity of YorkYorkUK

Bibliographic information