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We introduce both a novel technique to detect and describe 3D interest points
and a technique to use them for surface alignment.
Loosely Distinctive Features for Robust Surface Alignment
Andrea Albarelli, Emanuele Rodolà, and Andrea Torsello
Dipartimento di Informatica – Università Ca' Foscari di Venezia, Italy
Research Goal
Surface Registration
In this work we introduce a class of very simple multi-scale surface features called
Surface Hashes. These features show a very low distinctiveness in isolation, but
when coupled with a game-theoretic matching technique that exploits global
geometric consistency they allow to attain extremely robust surface registration.
We studied the sensitivity to noise,
occlusion and scale of different
Surface Hashes on both real-world
and synthetic objects
RMS Ratio is the ratio between the
RMS obtained after registration and
the RMS of ground truth alignment,
computed on the true correspondences
We compared our full pipeline with
DARCES and with a PROSAC-based
variant taking advantage of our
descriptors as a prior
System-level comparisons were
performed with the Spin Images and
MeshDOG/HOG pipelines
As the game is repeated, players will adapt their behavior to prefer matings that yield larger payoffs, driving all
inconsistent hypotheses to extinction. The search for a stable state is performed by simulating the evolution of
a natural selection process.
Experimental Results
We introduce a novel surface registration technique that uses very simple descriptors to create
several weak correspondence hypotheses that are further optimized by a robust game-theoretic
matcher. The approach is validated on a wide range of experiments showing its greater robustness
with respect to noise and occlusion in comparison with other well-known techniques.
Given a set of model points and a set of data points we call a mating strategy any pair with
and . We assign to each pair of mating strategies a payoff that is inversely proportional to a measure of
violation of a rigidity constraint.
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Surface Hashes
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Interest Points Detection
We define another Matching Game that ignores the information given by the descriptors and takes advantage
of the rigidity constraint to be enforced in the surface registration problem.
Conclusions
Surface Hashes are concise, point-wise descriptors which exhibit the property of being
highly repeatable at the cost of a relatively high probability of clashing.
The Normal Hash (on
the left) is the vector of
inner products between
a reference normal at a
large-scale patch and
the average normals
computed at smaller
scales.
The Integral Hash is the vector of approximated volume invariants of
spheres centered at the vertex. The volume is taken as the simple
sum of residuals over a least-squares fitted plane.
We screen out features exhibiting descriptors that are too common over the surface.
To do this, we define a Matching Game
where the strategy set S corresponds to
the set of all the surface points, and the
payoff matrix is defined by:
C(ij) = e^-a|di-dj|
The features that survive the evolutive
process are then removed from the set
of interest points.