MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching

Rafaël Brandt*, Nicola Strisciuglio, Nicolai Petkov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
49 Downloads (Pure)


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 MTStereo2.0, an improved version of the MTStereo stereo matching method, which includes a more robust context-driven cost function, better detection of incorrect matches and the computation of disparity at pixel level. MTStereo provides accurate sparse and semi-dense depth estimation and does not require intensive GPU computations. We tested it on several benchmark data sets, namely KITTI 2015, Driving, FlyingThings3D, Middlebury 2014, Monkaa and the TrimBot2020 garden data sets, and achieved competitive accuracy. The code is available at

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings
EditorsNicolas Tsapatsoulis, Andreas Panayides, Theo Theocharides, Andreas Lanitis, Andreas Lanitis, Constantinos Pattichis, Constantinos Pattichis, Mario Vento
Number of pages10
ISBN (Print)9783030891275
Publication statusPublished - 31 Oct 2021
Event19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021 - Virtual, Online
Duration: 28 Sept 202130 Sept 2021
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13052 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021
Abbreviated titleCAIP 2021
CityVirtual, Online


  • Max-Tree
  • Stereo matching


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