  Efficient yet accurate extraction of depth from stereo image pairs is
required by systems with low power resources, such as robotics and embedded
systems. State-of-the-art stereo matching methods based on convolutional neural
networks require intensive computations on GPUs and are difficult to deploy on
embedded systems. In this paper, we propose a stereo matching method, called
MTStereo 2.0, for limited-resource systems that require efficient and accurate
depth estimation. It is based on a Max-tree hierarchical representation of
image pairs, which we use to identify matching regions along image scan-lines.
The method includes a cost function that considers similarity of region
contextual information based on the Max-trees and a disparity border preserving
cost aggregation approach. MTStereo 2.0 improves on its predecessor MTStereo
1.0 as it a) deploys a more robust cost function, b) performs more thorough
detection of incorrect matches, c) computes disparity maps with pixel-level
rather than node-level precision. MTStereo provides accurate sparse and
semi-dense depth estimation and does not require intensive GPU computations
like methods based on CNNs. Thus it can run on embedded and robotics devices
with low-power requirements. We tested the proposed approach on several
benchmark data sets, namely KITTI 2015, Driving, FlyingThings3D, Middlebury
2014, Monkaa and the TrimBot2020 garden data sets, and achieved competitive
accuracy and efficiency. The code is available at
https://github.com/rbrandt1/MaxTreeS.
