A SCAN-TO-BIM WORKFLOW PROPOSAL FOR CULTURAL HERITAGE. AUTOMATIC POINT CLOUD SEGMENTATION AND PARAMETRIC-ADAPTIVE MODELLING OF VAULTED SYSTEMS by M. Buldo, L. Agustín-Hernández, C. Verdoscia, R. Tavolare

“…These types of structures are first examined using ideal models, which were perfectly discretised and set up by the user, and then employed as a starting point for validating the parameters of the RANSAC algorithm on point clouds acquired by laser scanners. …”
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Research on Visual Positioning of a Roadheader and Construction of an Environment Map by Wentao Zhang, Guodong Zhai, Zhongwen Yue, Tao Pan, Ran Cheng

“…In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. …”
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A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION by H. Ju

“…The from-coarse-to-fine hybrid co-registration approach includes LPFFT (Log-Polar Fast Fourier Transform), Harris Corners, PDF (Probability Density Function) feature descriptor mean-shift matching, and RANSAC (RANdom Sample Consensus) as main components. …”
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Enhancing the Localization Accuracy of UAV Images under GNSS Denial Conditions by Han Gao, Ying Yu, Xiao Huang, Liang Song, Li Li, Lei Li, Lei Zhang

“…Subsequently, a deep learning image registration network, combining SuperPoint and SuperGlue, was employed for high-precision feature point extraction and matching. The RANSAC algorithm was applied to eliminate mismatched points. …”
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Registration of Building Scan with IFC-Based BIM Using the Corner Points by Noaman Akbar Sheik, Peter Veelaert, Greet Deruyter

“…After extracting these corner points from both the as-built and the design model, a RANSAC-based pairwise assessment of the points is performed to identify potential matching points in both models using different discriminative geometric invariants. …”
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A Palletizing System for Microchannel Heat Exchangers Based on 3D Visual Guidance by Jiaze Chen, Songxiao Cao, Zhipeng Xu, Tao Song, Qing Jiang

“…Firstly, an automatic calibration method for RGB-D camera extrinsic parameters was adopted: used random sample consensus algorithm RANSAC for plane fitting, and the pallet was idealized as a plane. …”
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Metrological Characterization of a Vision-Based System for Relative Pose Measurements with Fiducial Marker Mapping for Spacecrafts by Marco Pertile, Sebastiano Chiodini, Riccardo Giubilato, Mattia Mazzucato, Andrea Valmorbida, Alberto Fornaser, Stefano Debei, Enrico C. Lorenzini

“…The measurement procedure comprises of a closed-form solution of the Perspective from n Points (PnP) problem, a RANdom SAmple Consensus (RANSAC) procedure, a non-linear local optimization and a global Bundle Adjustment refinement of the marker map and relative poses. …”
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A new denoising method for photon-counting LiDAR data with different surface types and observation conditions by Jieying Lao, Cheng Wang, Sheng Nie, Xiaohuan Xi, Hui Long, Baokun Feng, Zijia Wang

“…Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-step cannot be identified are further eliminated. …”
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Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching by Yizhi Cong, Yan Wang, Wenju Hou, Wei Pang

“…At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. …”
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Luojia-1 Nightlight Image Registration Based on Sparse Lights by Zhichao Guan, Guo Zhang, Yonghua Jiang, Xin Shen, Zhen Li

“…Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. …”
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A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving by Xianjian Jin, Hang Yang, Xin Liao, Zeyuan Yan, Qikang Wang, Zhiwei Li, Zhaoran Wang

“…The proposed algorithm is tested on our autonomous driving experimental platform and the public autonomous driving dataset KITTI, compared with the most used RANSAC algorithm and ray ground filter algorithm. …”
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Scale-Aware Multi-View Reconstruction Using an Active Triple-Camera System by Hang Luo, Christian Pape, Eduard Reithmeier

“…Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. …”
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CAISOV: Collinear Affine Invariance and Scale-Orientation Voting for Reliable Feature Matching by Haihan Luo, Kai Liu, San Jiang, Qingquan Li, Lizhe Wang, Wanshou Jiang

“…The results demonstrate that the proposed outlier-removal algorithm achieves the best overall performance when compared with RANSAC-like and local geometric constraint-based methods, and it can also be applied to achieve reliable outlier removal in the workflow of SfM-based UAV image orientation.…”
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High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner by Xia Huang, Shunyi Zheng, Ningning Zhu

“…Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. …”
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A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms by Samy Bakheet, Ayoub Al-Hamadi, Rehab Youssef

“…Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. …”
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A Coarse-to-Fine DLG and TLS Data Registration Method by Yunsheng Zhang, Ji Wu, Zhen Yang, Kailiang Deng

“…The proposed method is composed of five steps: 1) using the proposed method to extract the corner points from the building outline of DLG; 2) using the proposed method based on RANSAC algorithm to extract the corner points and the facade point clouds of the TLS data; 3) coarse alignment of the two extracted sets of corner points using four-point congruent set algorithm; 4) fine registration by 2D-ICP algorithm; and 5) completing elevation registration using elevation control points. …”
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CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network by Rahul Nandkumar Gore, Elena Lisova, Johan Åkerberg, Mats Björkman

“…As a software–based network time protocol (NTP) overcomes these challenges but lacks accuracy, the authors propose a software–based clock synchronization method, called CoSiWiNeT, based on the random sample consensus (RANSAC) algorithm that uses an iterative technique to estimate a correct offset from observed noisy data. …”
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Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan by John Noonan, Hector Rotstein, Amir Geva, Ehud Rivlin

“…In order to extract planes from point clouds that contain scale ambiguity, the <i>Scale Invariant Planar RANSAC</i> (SIPR) algorithm was developed. The best wall&#8315;plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. …”
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DETECTION OF HIDDEN EDGES AND CORNERS IN SLAM-BASED INDOOR POINT CLOUDS by J. Schmidt, V. Volland, D. Iwaszczuk, A. Eichhorn

“…In this work one approach based on contour point clouds and one approach based on planes are examined for the detection of corners and edges. Both approaches use RANSAC to extract either straight lines or planes. …”
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RELATIVE ORIENTATION IN LOW ALTITUDE PHOTOGRAMMETRY SURVEY by J. Wang, Z. Lin, C. Ren

“…In this paper, the Direct (D) relative orientation method is firstly used to get coarse values of the relative orientation parameters, then the Conventional (C) relative orientation process is implemented, using the coarse values as initial values in the first iterative calculation. And RANSAC (R) algorithm is finally applied in locating and extracting gross errors in relative orientation. …”
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