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3D Imaging with ToF Camera
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Time-of-Flight Principle
Reflected IR shows phase delay proportional to the distance
from the camera.
Time-of-flight of Light Distance
: It is not simple to measure the flight time directly at each pixel
of any existing image sensor
![Page 3: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/3.jpg)
Phase Delay Measurement
Q1 through Q4 are the amount of electrons measured at each
corresponding time.
In real situations, it is difficult to sense electric charge at certain
time instance
)(dt
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Phase Delay Measurement
Distance
21
43arctan2
)(2 QQ
QQcdt
c
21
43
21
43 arctan2
arctan2 qq
qqc
qqc
Assumption: Single reflected IR signal
In principle, amplitude of the reflected IR does not affect the depth
calculation.
![Page 5: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/5.jpg)
Multiple IR Signals
- Large Sensor Pixel
- Scattering
- Multipath
- Motion Blur
- Transparent Object
In real situations, multiple reflected IR signals with different phase
delays & amplitudes can be superposed.
)()(
)()(arctan
2)(
2211
4433
qqqq
qqqqcdt
We do not know how many IR signals will be superposed.
![Page 6: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/6.jpg)
Large Sensor Pixel
In order to increase sensitivity,
- large pixel size or pixel binning
IR signal #1
IR signal #2
)()(
)()(arctan
2)(
2211
4433
qqqq
qqqqcdt
![Page 7: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/7.jpg)
Multiple light reflections between the lens and the sensor
Light Scattering
Light scattering [1]
[1] “Real-time scattering compensation for time-of-flight camera”, CVS07
![Page 8: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/8.jpg)
Multipath Errors
IR LED
Sensor
Multipath Interference Depth error in concave objects
)()(
)()(arctan
2)(
2211
4433
qqqq
qqqqcdt
![Page 9: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/9.jpg)
Motion Blur
Moving camera/object within single integration time make wrong
depth calculation
Image sensor
Moving Object Moving Object
![Page 10: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/10.jpg)
Motion Blur
The characteristic of Tof motion blur is different from color
Overshoot Blur
Undershoot Blur
Overshoot Blur
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Motion Blur
We use a set of cycles for depth calculation
In motion blur case, multiple IR signals come in sequentially
Reflected IR #1 & #2
TimeInteg.
1Q
2Q
3Q
4Q
Emitted IR
))1(())1((
))1(())1((arctan
2)(
2211
4433
qnnqqnnq
qnnqqnnqcdt
![Page 12: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/12.jpg)
ToF Deblurring
Blur Detection
Blur Level
Input Depth
Deblurred Depth
- There are some relations between Q1~Q4 1Q
2Q
3Q
4Q
4321 QQQQ KQQQQ 4321
- We assume 2-Layer blur case
: single flat foreground + single flat background
![Page 13: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/13.jpg)
Transparent Object
2-Layer approximation of transparent object
))1(())1((
))1(())1((arctan
2)(
2211
4433
qqqq
qqqqcdt
- Sometimes 2-Layer is not enough
- Multiple reflection between objects (when they are close)
- In most cases, they have specular surface
![Page 14: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/14.jpg)
Transparent Object
)(
)(arctan
21
43
QQtd
)ˆˆ()(
)ˆˆ()(arctan
2121
4343
QQQQ
QQQQtd
Depth
IR-Intensity
Transparent object
Now, amplitude matters
![Page 15: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/15.jpg)
Transparent Object
)ˆˆ()(
)ˆˆ()(arctan
2121
4343
QQQQ
QQQQtd
![Page 16: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/16.jpg)
Transparent Object
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Due to the variation of the number of collected electrons during the integration time the repeatability of each depth point varies
Integration time-related Error
Integration Time: 30(ms) Integration Time: 80(ms)
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Due to the non-uniformity of IR illumination and reflectivity variation of objects use a polynomial fitting model
Amplitude-related Errors
Amplitude image of a planar object
with a ramp image. Parts of the ramp
are selected for calibration (blue
rectangle).
The depth samples (blue) and the
fitted model (green) to the error
x(pixel)
y(p
ixe
l)
Amplitude
Err
or(
m)
0
0.001
0.003
0.004
0.002
0
1 0.5
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Light attenuates according to the law of inverse square
Amplitude Correction
Distance-based intensity correction [18]
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Kinect Principle (1/3)
Basically, it is based on structured light principle
IR Speckle
Pattern
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Kinect Principle (2/3)
0. Calibrate source and
detector
1. Known IR pattern is
projected from the source
2. Detector identify each
dot (or set of dots)
3. Triangulate to calculate
depth
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Kinect Principle (3/3)
- Random speckles identify x,y locations
- Orientation and shape of the speckles change along distance
identify z location
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ToF vs Kinect
Kinect Fusion SAIT & KAIST using ToF Camera
3D Reconstruction using multiple depth images
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24
Depth/Point Cloud Processing
3D Features
3D Filtering
Registration Surface Processing
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Depth Distortion Upon Materials
• Conventional approaches assume the Lambertian materials.
• Various surface materials exhibit the complex light interaction, causing the non-linear distortion on light transport.
• Depth cameras suffer from the depth distortion upon material properties.
• The type of distortion varies upon the sensing principle of depth cameras.
25
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Depth Cameras
• We provide the distortion analysis based on two sensor types: A Time-of-Flight and a structured light sensor
[Swissranger] [Kinect]
26
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Depth Distortion – Lambertian
• Material affects the sensing performance (Lambertian)
All existing 3D sensing
techniques are limited to
Lambertian object. Sensor
IR LED
Sensor
Projector
ToF depth camera Structured light depth camera
27
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Depth Distortion – Specularity
• Non-Lambertian materials causes the failure in sensing reflected signal (Specularity)
Sensor
IR LED
Sensor
Projector
ToF depth camera Structured light depth camera
28
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Depth Distortion – Translucency
• Non-Lambertian materials causes the failure in sensing reflected signal (Translucency)
Sensor
Projector
Sensor
IR LED IR LED
ToF depth camera Structured light depth camera
29
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Depth Distortion – Global Illumination
• Complex illumination affects the sensing performance (Global Illumination)
Sensor
Projector
Sensor
IR LED IR LED
ToF depth camera Structured light depth camera
30
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Depth Error – Specularity
• ToF depth camera
31
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Depth Error – Translucency
• ToF depth camera
32
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Depth Error – Specularity
• Structured light depth camera
33
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Depth Error – Translucency
• Structured light depth camera
34
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Depth Error Analysis
35
• Data collection & analysis
![Page 36: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/36.jpg)
Depth Error Analysis
ToF Sensor
36
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Depth Error Analysis
Kinect Sensor
37
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Color-Depth Calibration
![Page 39: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/39.jpg)
Given a calibrated TOF-Stereo system
• Each TOF point PT defines a correspondence between PL and PR
![Page 40: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/40.jpg)
Correspondences (samples) obtained by using the calibration parameters
• each correspondence comes from a TOF point
• different color -> different depth
![Page 41: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/41.jpg)
Correspondences (samples) obtained by using the calibration parameters
• each correspondence comes from a TOF point
• different color -> different depth
![Page 42: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/42.jpg)
TOF-to-Left Mapping
• We use the left image as reference
![Page 43: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/43.jpg)
TOF-to-Left Mapping
Resolution mismatch
![Page 44: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/44.jpg)
Left-to-Tof Occlusions
TOF-to-Left Mapping
Left-to-Tof Occlusions: the depth decreases from left to right
![Page 45: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/45.jpg)
Tof-to-Left Occlusions
TOF-to-Left Mapping
Tof-to-Left Occlusions: the depth increases from left to right
![Page 46: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/46.jpg)
Point Cloud filtering
• We reject points in left-to-tof occluded area
• We keep the minimum-depth points in case of overlap (due to Tof-to-left occlusions)
![Page 47: 3D Imaging with ToF Camera - khu.ac.krcvlab.khu.ac.kr/CVLecture24.pdf · Tof-to-Left Occlusions: the depth increases from left to right . Point Cloud filtering •We reject points](https://reader030.vdocuments.net/reader030/viewer/2022040106/5ecf2f98dbbbed738d71cb88/html5/thumbnails/47.jpg)
Disparity Map: Initialization
• Run Delauney-Triangulation on low-resolution point cloud
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Disparity Map: Initialization
• Run Delauney-Triangulation on low-resolution point cloud…
• …and initialize the stereo disparity map