SLIDE 1
- Models
- Sensor
- Point clouds
- Mesh from cloud
CMSC427 Model capture Creating 3D mesh from real world data Models - - PowerPoint PPT Presentation
CMSC427 Model capture Creating 3D mesh from real world data Models - - PowerPoint PPT Presentation
CMSC427 Model capture Creating 3D mesh from real world data Models Sensor Point clouds Mesh from cloud How? Collect data 2D images 2 1/2 images Point clouds 1D scans Align and fuse data Create mesh from
SLIDE 2
SLIDE 3
- Collect data
- 2D images
- 2 1/2 images
- Point clouds
- 1D scans
- Align and fuse data
- Create mesh from data
- Data to point cloud
- Point cloud to mesh
How?
SLIDE 4
Collect data
- 2D images
- Sequence or
- simultaneously
4
SLIDE 5
2 ½ D scanner
- Eg, MS Kinect. Image plus depth data (2 ½ D)
- Structure.io
5
SLIDE 6
1-D scanners
- Eg, Eora3D.com
- Demo’d in class:
Hokuyo URG-04LX
6
SLIDE 7
Point scanners
- Contact scanner
- Laser range scanner
- Adjustable sequence of
points
SLIDE 8
T wo primary approaches
- Time of flight
- LIDAR – Light Detection and Ranging
SLIDE 9
T wo primary approaches
- Triangulation
- Camera to camera (stereo)
- Laser to camera
- Projector to camera
- Problem: matching
SLIDE 10
Kinect – structured projected infrared light
- Emitter projects
known pattern
- Helps in matching
SLIDE 11
Align and fuse data
- Match data between images
- Or point clouds
SLIDE 12
Matching image points
- Find distinctive points in each image
- Match based on
local properties
SLIDE 13
Imtermediate objective – fused point cloud
SLIDE 14
Final objective – point cloud to mesh
- One tool: Meshlab