Đồ án tốt nghiệp - Generate 3D model of objects from images

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND
EDUCATION
FALCUTY OF INTERNATIONAL EDUCATION

GRADUATION PROJECT
COMPUTER ENGINEERING TECHNOLOGY

GENERATE 3D MODEL OF OBJECTS
FROM IMAGES

Student name: Tăng Hiệp Vy Quí
Student ID: 19119055
Advisor: Associate professor PhD Trương Ngọc Sơn

HO CHI MINH CITY – 7/2024

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND
EDUCATION
FALCUTY OF INTERNATIONAL EDUCATION

GRADUATION PROJECT
COMPUTER ENGINEERING TECHNOLOGY

GENERATE 3D MODEL OF OBJECTS
FROM IMAGES

Student name: Tăng Hiệp Vy Quí
Student ID: 19119055
Advisor: Associate professor PhD Trương Ngọc Sơn

HO CHI MINH CITY – 7/2024

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness
-Ho Chi Minh City, June 24, 2024

GRADUATION PROJECT ASSIGNMENT
Student Name: Tăng Hiệp Vy Quí

Student ID: 19119055

Major: Computer Engineering Technology

Class: 19119CLA

Advisor: Associate professor PhD Trương Ngọc Sơn
Date of assignment: / /2024

Date of submission : / /2024

1. Project title: Generate 3D model of objects from images
2. Initial materials provided by the advisor: None
3. Content of the project:
-

Build a system to collect data images base on photogrammetry.

-

Control exposure level for high detail quality

-

Build a pipeline to structure from motion then generate point cloud and
surface

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Images collected and reconstructed in different Structure from motion
software

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Compare against existing methods.

4. Final product: Apply the Structure from Motion pipeline to reconstruct images into
a 3D model. The final product fulfils the original purpose: When a set of images is
captured using photogrammetry theory and input into open-source 3D reconstruction
software, the system will output a corresponding high-resolution model.
CHAIR OF THE PROGRAM

ADVISOR

(Sign with full name)

(Sign with full name)

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness
-Ho Chi Minh City, June 24, 2024

ADVISOR’S EVALUATION SHEET
Student Name: Tăng Hiệp Vy Quí

Student ID: 19119055

Major: Computer Engineering Technology

Class: 19119CLA

Project title: Generate 3D model of objects from images
Advisor: Associate professor PhD Trương Ngọc Sơn
EVALUATION

1. Content of the project
:
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...................................................................................
...

...
ation
techniques can simplify the process of creating and manipulating 3D models.
By pursuing these avenues of research and development, this thesis aims to
contribute to the advancement of 3D reconstruction technology, fostering
innovation in fields ranging from cultural heritage preservation to industrial
applications. Embracing emerging technologies and methodologies will be
essential in harnessing the full potential of computer vision for generating detailed
and realistic 3D models from images.
In conclusion, while this thesis has laid a solid foundation, the journey towards
comprehensive and efficient 3D reconstruction is ongoing, promising continued
advancements and exciting possibilities in the realm of computer vision and
beyond.

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Refference
[1] Richard Szeliski, “Computer Vision: Algorithms and Applications 2nd Edition
September 30, 2021”
[2] Maarten Vergauwen, “3D Reconstruction from Multiple Images, Foundations
and Trends® in Computer Graphics and Vision Series, 2009”
[3] Manas Kamal Bhuyan, “Computer Vision and Image Processing Fundamentals
and Applications 1st Edition 2020”
[4] M. Sonka, V. Hlavac, and R. Boyle, “Image processing, analysis and machine
vision. Cengage Learning, 2007”
[5] Li Ling, M.Eng, “Dense Real-time 3D Reconstruction from Multiple Images
August 2013”
[6] R. I. Hartley, “In Defense of the Eight-Point Algorithm,” IEEE Transaction on
Pattern Recognition and Machine Intelligence, vol. 19, no. 6, p. 14, 1997.
[7] Cornell University, Indian Institute of Technology Roorkee, “Deep
Fundamental Matrix Estimation without Correspondences”
[8] Po-Han Lee, Jui-Wen Huang, Huei-Yung Lin, “3D Model Reconstruction
Based on Multiple View Image Capture”, 2012 IEEE International Symposium on
Intelligent Signal Processing and Communication Systems November 4-7, 2012
[9] E. Rosten and T. Drummond, “Machine Learning for Highspeed Corner
Detection,” In European Conference on Computer Vision, 2006.
[10] Jingming Dong, Stefano Soatto, “Domain-Size Pooling in Local Deors:
DSP-SIFT” UCLA Vision Lab, University of California, Los Angeles, CA 90095
[11] Lester Kalms, Khaled Mohamed, Diana Göhringer, “Accelerated Embedded
AKAZE Feature Detection Algorithm on FPGA”
[12] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, “SURF: Speeded Up
Robust Features”
[13] Deepak Geetha Viswanathan “Features from Accelerated Segment Test
(FAST)”
[14] Pablo Fern´andez Alcantarilla1, Adrien Bartoli, and Andrew J. Davison
“KAZE Features“
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