Journal ArticleDOI
An Optimized BaySAC Algorithm for Efficient Fitting of Primitives in Point Clouds
TLDR
A method for fitting primitives that fuses the Bayesian sample consensus (BaySAC) algorithm with a statistical testing of candidate model parameters for unorganized 3-D point clouds and the strategy of prior probability determination is proven to be model-free and, thus, highly applicable.Abstract:
Fitting primitives is of great importance for remote sensing applications, such as 3-D modeling and as-built surveys This letter presents a method for fitting primitives that fuses the Bayesian sample consensus (BaySAC) algorithm with a statistical testing of candidate model parameters for unorganized 3-D point clouds Instead of randomly choosing initial data sets, as in the random sample consensus (RANSAC), we implement a conditional sampling method, which is the BaySAC, to always select the minimum number of data required with the highest inlier probabilities As the primitive parameters calculated by the different inlier sets should be convergent, this letter presents a statistical testing algorithm for the histogram of the candidate model parameter to compute the prior probability of each data point Moreover, the probability update is implemented using the simplified Bayes formula The proposed approach is tested with the data sets of planes, tori, and curved surfaces The results show that the proposed optimized BaySAC can achieve high computational efficiency (five times higher than the efficiency of the RANSAC for fitting a subset of 12 500 points) and high fitting accuracy (on average, 20% higher than the accuracy of the RANSAC) Moreover, the strategy of prior probability determination is proven to be model-free and, thus, highly applicableread more
Citations
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Journal ArticleDOI
Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds
TL;DR: The results show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average).
Journal ArticleDOI
Mobile Laser Scanner data for automatic surface detection based on line arrangement
TL;DR: The singular geometry of the MLS data on planar surfaces is used to transform the original point cloud into a more structured line cloud, which allows the simplification of the initial data and identification of surfaces by grouping lines.
Journal ArticleDOI
A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data
TL;DR: A comparative scheme, which investigates a popular deep learning (deep Boltzmann machine, DBM) model for high-level feature representation and widely used machine learning methods for low- level feature extraction and selection [principal component analysis (PCA) and random forest) in land cover classification, demonstrated that the classification accuracies of the DBM-based method were higher than those of the RF-based and PCA-based methods using multispectral LiDAR data.
Journal ArticleDOI
Robust point cloud registration based on topological graph and Cauchy weighted lq-norm
TL;DR: A robust and efficient PCR method based on topological graph and Cauchy weighted l q -norm is proposed, which is highly robust to outliers and partial overlaps and much faster than compared state-of-the-art methods.
References
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Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: 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 that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
TL;DR: A new robust estimator MLESAC is presented which is a generalization of the RANSAC estimator which adopts the same sampling strategy as RANSac to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers.
Journal ArticleDOI
Efficient RANSAC for Point-Cloud Shape Detection
TL;DR: An automatic algorithm to detect basic shapes in unorganized point clouds based on random sampling and detects planes, spheres, cylinders, cones and tori, and obtains a representation solely consisting of shape proxies.
Book
Bayesian Statistics: An Introduction
TL;DR: This new fourth edition of Peter Lees book looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo, providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach.
Journal ArticleDOI
Determination of terrain models in wooded areas with airborne laser scanner data
K. Kraus,Norbert Pfeifer +1 more
TL;DR: In this article, the characteristics of laser scanning are compared to photogrammetry with reference to a big pilot project and the results are in accordance with the expectations, however, the geomorphologic quality of the contours, computed from a terrain model derived from laser scanning, needs to be improved.