Fast locally consistent dense stereo on multicore

Many computer vision applications require fast and accurate
3D measurements. However, despite the advent of
powerful computing architectures (e.g., multicore CPU and
GPU), most top-ranked dense stereo algorithms rely on
global 2D disparity optimization methods that are often
too slow for practical use. Moreover, their huge memory
requirements are typically not suited to devices with constrained
resources (e.g., FPGA). Nevertheless, algorithms
based on 1D disparity optimization methods (ı.e., Dynamic
Programming and Scanline Optimization) provide a good
trade-off between accuracy and efficiency with a limited
memory footprint. In this paper, we show that enforcing a
relaxed local consistency constraint to the disparity fields,
provided by fast 1D disparity optimization methods, yields
much more rapidly, results comparable to those of the topranked
approaches. The simple and non-iterative computational
structure of our proposal enables us to exploit coarse
grained parallelism on multicore CPUs. Moreover, due to
its limited memory footprint, our proposal could be potentially
mapped on devices, such as FPGA, with constrained
resources.