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...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2022
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Online Access: | https://hdl.handle.net/10356/156617 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
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