Communications of the ACM / 1981

Random sample consensus

Martin A. Fischler, Robert C. Bolles

Computer VisionFoundation Models

A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

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Title: Random sample consensus
Authors: Martin A. Fischler, Robert C. Bolles
Year: 1981
Venue: Communications of the ACM
Categories: Computer Vision, Foundation Models
Citations: 25,258
Paper URL: https://doi.org/10.1145/358669.358692
Open PDF: https://dl.acm.org/doi/pdf/10.1145/358669.358692

Abstract:
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Random%20sample%20consensus)
- OpenAlex: Paper page (https://doi.org/10.1145/358669.358692)
- PDF: Open access PDF (https://dl.acm.org/doi/pdf/10.1145/358669.358692)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Random%20sample%20consensus)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Random%20sample%20consensus+paper+explained)