Comparing NARF and SIFT Key
Point Extraction Algorithms
Chris Kaffine
Second Annual MIT PRIMES Conference, May
20th, 2012
Range Sensors

Purpose: collect distance information

Advantage over cameras: 3D

Methods:

Stereo Imagery

LiDAR

Structured Light
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
Correspondences


Goal: Find points in two images which are
equivalent
With matched points, differences between
images can be calculated
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
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
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”
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
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
Results- Measures of Success
Measure 1: Smoother, NARF exceeds SIFT in parts
 Overall, similar trends, though distinct metrics

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

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

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

Acknowledgements

MIT PRIMES

Professor Seth Teller

Jon Brookshire – Mentor