3D image reconstruction based on current stereo vision techniques
3D image reconstruction has become progressively popular in recent years with its application ranging from facial recognition in smart phones, measuring the deformation of an object, and even to reconstructing 3D environments in autonomous vehicles. Most of the 3D image reconstruction techniques...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156617 |
| _version_ | 1824454569025863680 |
|---|---|
| author | Chan, Favian Jun Wei |
| author2 | Qian Kemao |
| author_facet | Qian Kemao Chan, Favian Jun Wei |
| author_sort | Chan, Favian Jun Wei |
| collection | NTU |
| description | 3D image reconstruction has become progressively popular in recent years with its
application ranging from facial recognition in smart phones, measuring the deformation of an
object, and even to reconstructing 3D environments in autonomous vehicles. Most of the 3D
image reconstruction techniques is derived based on the principle of stereo vision. Stereo
vision is based on the term stereopsis which refer to how our eyes perceive depth.
There is an underlying debate in the autonomous vehicle industry between the use of active
setup like LiDAR and passive setup with just cameras. LiDAR is popular within the
autonomous vehicle industry due to its high accuracy and reliability when detecting objects.
However, LiDAR is expensive and bulky to apply in autonomous vehicle. Instead, using a
passive stereo setup will be much cheaper, smaller and easier to apply. However, passive
stereo setup is susceptible to objects that have weak texture. Thus, we would like to find out
to what extend does texture affect the accuracy of the 3D image reconstruction in a passive
stereo setup.
In this research project we will understand the principle of stereo vision and construct a
stereo vision system to examine how textures affect the 3D image reconstruction. This project
will look at a traditional stereo vision technique that is implemented using OpenCV and also
compare the results we get to a deep learning stereo vision technique. |
| first_indexed | 2025-02-19T03:24:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156617 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:24:23Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1566172022-04-21T05:30:37Z 3D image reconstruction based on current stereo vision techniques Chan, Favian Jun Wei Qian Kemao School of Computer Science and Engineering MKMQian@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision 3D image reconstruction has become progressively popular in recent years with its application ranging from facial recognition in smart phones, measuring the deformation of an object, and even to reconstructing 3D environments in autonomous vehicles. Most of the 3D image reconstruction techniques is derived based on the principle of stereo vision. Stereo vision is based on the term stereopsis which refer to how our eyes perceive depth. There is an underlying debate in the autonomous vehicle industry between the use of active setup like LiDAR and passive setup with just cameras. LiDAR is popular within the autonomous vehicle industry due to its high accuracy and reliability when detecting objects. However, LiDAR is expensive and bulky to apply in autonomous vehicle. Instead, using a passive stereo setup will be much cheaper, smaller and easier to apply. However, passive stereo setup is susceptible to objects that have weak texture. Thus, we would like to find out to what extend does texture affect the accuracy of the 3D image reconstruction in a passive stereo setup. In this research project we will understand the principle of stereo vision and construct a stereo vision system to examine how textures affect the 3D image reconstruction. This project will look at a traditional stereo vision technique that is implemented using OpenCV and also compare the results we get to a deep learning stereo vision technique. Bachelor of Engineering (Computer Science) 2022-04-21T05:30:37Z 2022-04-21T05:30:37Z 2022 Final Year Project (FYP) Chan, F. J. W. (2022). 3D image reconstruction based on current stereo vision techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156617 https://hdl.handle.net/10356/156617 en SCSE21-0481 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Chan, Favian Jun Wei 3D image reconstruction based on current stereo vision techniques |
| title | 3D image reconstruction based on current stereo vision techniques |
| title_full | 3D image reconstruction based on current stereo vision techniques |
| title_fullStr | 3D image reconstruction based on current stereo vision techniques |
| title_full_unstemmed | 3D image reconstruction based on current stereo vision techniques |
| title_short | 3D image reconstruction based on current stereo vision techniques |
| title_sort | 3d image reconstruction based on current stereo vision techniques |
| topic | Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/156617 |
| work_keys_str_mv | AT chanfavianjunwei 3dimagereconstructionbasedoncurrentstereovisiontechniques |