Graph-Cut RANSAC
By Daniel Barath, Jiri Matas. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
391 citations recorded by the source corpus.
A novel method for robust estimation, called Graph-Cut RANSAC1, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard CPU).
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/6c0f45f3f05265f72ccce3448530a84fbc7816a7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Eduard Trulls
- Kwang Moo Yi
- Pascal Fua
- Jiri Matas
- Dmytro Mishkin
- Anastasiia V. Mishchuk
- Marc Pollefeys
- Zixin Luo
In Computer Vision and Pattern Recognition
- Eduard Trulls
- Pascal Fua
- Kwang Moo Yi
- Luc Van Gool
- Andrew Zisserman
- Marc Pollefeys
- Andrea Vedaldi
- Dacheng Tao
Citations in the corpus
- Image Matching Across Wide Baselines: From Paper to Practice (2020, methodology): “In addition to (locally-optimized) RANSAC [39, 29], as implemented in OpenCV [23], and sklearn [70], we consider more recent algorithms with publicly available implementations: DEGENSAC [30], GC-RANSAC [14] and MAGSAC [15].”
- PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency (2021, methodology): “We first select four representative traditional methods: FGR [77], SM [36], RANSAC [24], and GC-RANSAC [5], as well as the state-of-the-art geometrybased method TEASER [66].”
- ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning (2020, background): “However, most research papers on this topic focus on estimating the Fundamental matrix [9, 10, 38, 3, 4], which is why we focus on this problem instead.”
- MAGSAC++, a Fast, Reliable and Accurate Robust Estimator (2019, methodology): “The compared methods are RANSAC [5], LMedS [22], MSAC [28], GC-RANSAC [1], MAGSAC [2], and MAGSAC++.”