Abstract:
As the most computationally intensive part of a
stereo vision system, stereo matching has been the focus of intense
research activities for the last four decades. We present the first
attempts to compare a quantum stereo matching solution with
state-of-the-art approaches on the Middlebury stereo datasets.
We first looked at quantum annealing computation as a way to
interact with the D-Wave quantum computer and then improved
the quantum solution to the stereo matching problem found in
the literature. Using a line-by-line approach, we traded accuracy
off for the qubits availability in the QPU (Quantum Processor
Unit). Our findings show that it is possible to obtain results from
real-sized images despite the scarcity of physical qubits in the
quantum hardware. While the current quantum solution solves a
class P stereo matching problem, its real advantage over classical
stereo matching algorithms will arise with NP-hard problems
which will be the focus of our future research.