1. Comparing NARF and SIFT Key Point Extraction Algorithms
Chris Kaffine
Second Annual MIT PRIMES Conference, May 20th, 2012

2. Range Sensors Purpose: collect distance information
Advantage over cameras: 3D
Methods:
Stereo Imagery
LiDAR
Structured Light

3. Representing Range Data
Point Clouds:
3D-coordinates
Geometrically understandable
Range Images:
2D-image with pixel values representing depth
Similar to sensor functioning
Allows border extraction

4. Correspondences Goal: Find points in two images which are equivalent
With matched points, differences between images can be calculated

5. Key Points and Descriptors
Find correspondences in two steps: find key points, calculate descriptors
Key Points- Distinguishable, stable locations in a scene
Descriptors- Numerical description of a point and its underlying surface
Points with similar descriptors are correspondences

6. NARF Normally Aligned Radial Features Uses range images
Uses borders and change in distance (pixel) values to identify key points
Key points are invariant to scale, susceptible to camera orientation
Support Size: indicates how detailed the search should be

7. SIFT Scale Invariant Feature Transform Uses point clouds
Finds key points that are invariant to scale
Utilizes full, 3D geometry
Scale Size: indicates how close to “zoom in”

8. Evaluating the Algorithms
Use data with known sensor location
Within chronologically adjacent frames, search for nearby key points
Points within a certain distance are considered true matches
Count number of frames each point lasts for
Repeat, using different algorithms with different parameter values and different distance thresholds

15. Evaluating the Algorithms
Metrics for evaluation:
Number of key points identified
Persistence/Stability of key points
Density of key points, with relation to distance threshold
Due to limitations in persistence algorithm, two persistence metrics were used:
Measure 1:Average persistence of all key points
Measure 2: Number of key points with persistence greater than 1

16. Results- Measures of Success
Measure 1: Smoother, NARF exceeds SIFT in parts
Overall, similar trends, though distinct metrics

17. Results- Measures of Success
Measure 1: Smoother, NARF exceeds SIFT in parts
Overall, similar trends so overestimation most likely did not have a strong effect

18. Results- Measures of Success
At low parameter values, SIFT key point numbers and density rise dramatically, NARF values rise steadily
Indicates that as parameter values decrease, superfluous key points are detected

19. Results Best parameter values for each algorithm displayed
Metric used: #key points * persistence / density
SIFT almost always superior
Scale size .07 better in general, 0.1 possibly better in some cases

20. Acknowledgements MIT PRIMES Professor Seth Teller
Jon Brookshire – Mentor