Image Based 3D Modeling
以二維影像資料重建三維物體模型
指導教授:劉興民 教授
專題生:張鈞皓、蕭宥騰、裴家佑
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Outline
• Introduction
• Data flow & Environments
• Method
– Bundler
– PMVS
• Mesh
• Result
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What is this Image Based 3D
Modeling?
An automatic process which transforms photos
into a virtual 3D model.
Automatic Modeling
Process
Photos
3D model
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Motivation:
• Those modeling methods are old fashioned.
For example:
1. Artificial modeling spends too much
time.
2. Special modeling hardware costs too
much.
• A fast modeling method significantly improves
development of virtual reality.
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Implementation Outline
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Environments
• 開發系統： Cygwin on Windows (C/C++)
• 相關工具：
– Meshlab
– Imagemagick
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Implementation method
Input: 對目標環場拍攝之照片(15~20張、照片
中之物體必須有重疊性存在)
步驟:
1.讀取照片EXIF資訊並分析拍攝相對位置
2.從二維相片找出三維資訊
3.輸出圖片關係
4.corner detection進階特徵分析
5.三角化重建產生密點雲
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Implementation method
• Bundler：
– 實作Structure form motion
• PMVS：
– 利用照片資訊，產生點雲
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Structure from motion
• 從二維影像得到三維的資訊（影像間須有
重疊性）
• 觀察者和物體必須有相對運動
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特徵點分析
• Use SIFT
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判斷點相關性
• 建立K-dimension tree
– 找出涵蓋範圍最大的cut dimension，以此cut
dimension的數值為依據建立子樹
• 方法一：以平均值將點分給兩邊子樹
• 方法二：將點平均分配給兩邊子樹，即找尋點的中
位數
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Improvement
We have modified the feature detection method of API
to improve the quality of result.
Before
After
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特徵點對應
• approximate nearest neighbors
• RANdom SAmple Consensus
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corner detection
• Harris corner detector
• Difference of Gaussian
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Expand the feature points
• Use Triangular Reconstruct
– 相鄰的點具有相似的法向量與位置
– 過濾處理，剔除灰度一致性、幾何一致性較弱
的面
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Mesh
• Use meshlab
• method：
– Ball pivoting
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Result
The final result is a point cloud.
Point cloud is consisted of lots of points.
Each points is represented by 3D coordinate and color.
Photos
3D model
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Comparison between different
resolutions
The resolution has positive relation to the quality of
result and processing time.
80%
60%
40%
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END
• Thank you for listening
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