In this paper we investigate an emerging appli-
cation, 3D scene understanding, likely to be significant in the
mobile space in the near future. The goal of this exploration
is to reduce execution time while meeting our quality of result
objectives. In previous work, we showed for the first time that
it is possible to map this application to power constrained
embedded systems, highlighting that decision choices made at
the algorithmic design-level have the most significant impact.
As the algorithmic design space is too large to be exhaus-
tively evaluated, we use a previously introduced multi-objective
random forest active learning prediction framework dubbed
HyperMapper, to find good algorithmic designs. We show
that HyperMapper generalizes on a recent cutting edge 3D
scene understanding algorithm and on a modern GPU-based
computer architecture. HyperMapper is able to beat an expert
human hand-tuning the algorithmic parameters of the class
of computer vision applications taken under consideration in
this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the
Pareto front obtained on an embedded system can be used to
accelerate the same application on all the 83 smart-phones and
tablets with speedups ranging from 2x to over 12x.