Overview of Pont Cloud library for 2D/3D image processing and recognition

1. POINT CLOUD
LIBRARY

2. Point Cloud Library(PCL)
 Large scale, open project for 2D/3D
image and point cloud processing
 cross-platform
 Modular
 Contains numerous algorithm used to
extract key points, compute descriptors
to recognize objects etc.

3. Automated 3D-Model Identification
using PCL
 Point cloud data creation
 Key-point Extraction
 Descriptors
 Matching (KNN)
Point cloud
data
Key-point
Extraction
Descriptor Matching

4. Point Cloud Data
 A point cloud is a data
structure used to represent a
collection of multi-
dimensional points
3D-Point Cloud
Code Representation

5. Key-point Extraction
 Invariant to translation, rotation

6. Descriptors/Features
 Pointwise descriptors
 Simple, efficient, but not robust to
noise, often not descriptive
enough
 Local/Regional descriptors
 Locality: features are local
 Distinctiveness: can differentiate a
large database of objects
 Global descriptors
 Higher invariance, well suited for
retrieval and categorization
 More descriptive on objects with
poor geometric structure
Histogram Based Graph Based

7. Matching
 KNN algorithms are
used
 K-Nearest
Neighbours
 Non-parametric
method for
regression and
classification
 K : number of close
neighbours
N(0)>N(a), so c belongs to o

8. References
1.Point Cloud Library. http://pointclouds.org/about/
2. Federico Tombari. June 4, 2013. Keypoints and Features.
http://www.pointclouds.org/assets/uploads/cglibs13_features.pdf
3. Features and Image Matching.
https://courses.cs.washington.edu/courses/cse455/09wi/Lects/lect6.pdf