Diagnostic-robust statistical analysis for Local Surface Fitting in 3D Point Cloud Data

Objectives: Surface reconstruction and fitting for geometric primitives and three Dimensional (3D) modeling is a fundamental task in the field of photogrammetry and reverse engineering. However it is impractical to get point cloud data without outliers/noise being present. The noise in the data acquisition process induces rough and uneven surfaces, and reduces the precision/accuracy of the acquired model. This paper investigates the problem of local surface reconstruction and best fitting from unorganized outlier contaminated 3D point cloud data. Methods: Least Squares (LS) method, Principal Component Analysis (PCA) and RANSAC are the three most popular techniques for fitting planar surfaces to 2D and 3D data. All three methods are affected by outliers and do not give reliable and robust parameter estimation. In the statistics literature, robust techniques and outlier diagnostics are two complementary approaches but any one alone is not sufficient for outlier detection and robust parameter estimation. We propose a diagnostic-robust statistical algorithm that uses both approaches in combination for fitting planar surfaces in the presence of outliers.Robust distance is used as a multivariate diagnostic technique for outlier detection and robust PCA is used as an outlier resistant technique for plane fitting. The robust distance is the robustification of the well-known Mohalanobis distance by using the recently introduced high breakdown Minimum Covariance Determinant (MCD) location and scatter estimates. The classical PCA measures data variability through the variance and the corresponding directions are the latent vectors which are sensitive to outlying observations. In contrast, the robust PCA which combines the 'projection pursuit' approach with a robust scatter matrix based on the MCD of the covariance matrix, is robust with outlying observations in the dataset. In addition, robust PCA produces graphical displays of orthogonal distance and score distance as the by-products which can detects outliers and aids better robust fitting by using robust PCA for a second time in the final plane fitting stage. In summary, the proposed method removes the outliers first and then fits the local surface in a robust way.Results and conclusions: We present a new diagnostic-robust statistical technique for local surface fitting in 3D point cloud data. Finally, the benefits of the new diagnostic-robust algorithm are demonstrated through an artificial dataset and several terrestrial mobile mapping laser scanning point cloud datasets. Comparative results show that the classical LS and PCA methods are very sensitive to outliers and failed to reliably fit planes. The RANSAC algorithm is not completely free from the effect of outliers and requires more processing time for large datasets. The proposed method smooths away noise and is significantly better and efficient than the other three methods for local planar surface fitting even in the presence of roughness. This method is applicable for 3D straight line fitting as well and has great potential for local normal estimation and different types of surface fitting

Robust segmentation in laser scanning 3D point cloud data

Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods

Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks

MLS-assisted validation of WorldView-2 panchromatic image for estimating Pinus sylvestris crown height

High spatial resolution satellite imaging has the advantages of both fine scale and large coverage that indicate the potential for measuring forest morphologies. However, because of the aerial view, imaging has limited capacity of explicitly deriving the under-crown structural parameters. A possible solution is to explore the relationships between this kind of variables such as crown height (CH) and the feature parameters readily derived from the satellite images. However, field sampling of the training data is not a trivial task. To handle this issue, this study attempted the state-of-the-art remote sensing technology of vehicle-based mobile laser scanning (MLS) for collecting the sample data. Evaluation for the case of the Scots pine (Pinus sylvestris) trees has preliminarily validated the plan. That is, MLS mapping enabled the parameter of CH to be estimated from WorldView-2 panchromatic images

Not just for the wealthy: Rethinking farmed fish consumption in the Global South

Aquaculture’s contributions to food security in the Global South are widely misunderstood. Dominant narratives suggest that aquaculture contributes mainly to international trade benefiting richer Northern consumers, or provides for wealthy urban consumers in Southern markets. On the supply side, the literature promotes an idealized vision of ‘small-scale’, low input, semi-subsistence farming as the primary means by which aquaculture can contribute to food security, or emphasizes the role of ‘industrial’ export oriented aquaculture in undermining local food security. In fact, farmed fish is produced predominantly by a ‘missing middle’ segment of commercial and increasingly intensive farms, and overwhelmingly remains in Southern domestic markets for consumption by poor and middle income consumers in both urban and rural areas, making an important but underappreciated contribution to global food security

Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account

Let them eat carp: Fish farms are helping to fight hunger

First paragraph: Over the past three decades, the global aquaculture industry has risen from obscurity to become a critical source of food for millions of people. In 1990, only 13 percent of world seafood consumption was farmed; by 2014, aquaculture was providing more than half of the fish consumed directly by human beings.  The boom has made farmed fish like shrimp, tilapia and pangasius catfish – imported from countries such as Thailand, China and Vietnam – an increasingly common sight in European and North American supermarkets. As a result, much research on aquaculture has emphasized production for export.  This focus has led scholars to question whether aquaculture contributes to the food security of poorer people in producing countries. Many have concluded it does not. Meanwhile, the industry’s advocates often emphasize the potential for small-scale farms, mainly growing fish for home consumption, to feed the poor. Farms of this kind are sometimes claimed to account for 70 to 80 percent of global aquaculture production

Automated Matching of Segmented Point Clouds to As-built Plans

Terrestrial laser scanning (TLS) is seeing an increase use for surveying and engineering applications. As such, there is much on-going research into automating the process for segmentation and feature extraction. This paper presents a simple method for segmenting the interior of a building and comparing it to as-built plans. The method is based on analysing the local point attributes such as curvature, surface normal direction and underlying geometric structure. Random sampling consensus (RANSAC), region growing and voting techniques are applied to identify the predominant salient surface feature to extract wall and vertical segments. This information is used to generate a 2D plan of the interior space. A distance weighted method then automatically locates the corresponding vertices between the different datasets to transform them into a common coordinate system.A traditional survey was performed alongside the 3D point cloudcapture to compare and validate the generated 2D plans and the comparison to the existingdrawings. The accuracy of such generated plans from 3D point clouds will be explored

The Increasing Rotation Period of Comet 10P/Tempel 2

We imaged comet 10P/Tempel 2 on 32 nights from 1999 April through 2000 March. R-band lightcurves were obtained on 11 of these nights from 1999 April through 1999 June, prior to both the onset of significant coma activity and perihelion. Phasing of the data yields a double-peaked lightcurve and indicates a nucleus rotational period of 8.941 +/- 0.002 hr with a peak-to-peak amplitude of ~0.75 mag. Our data are sufficient to rule out all other possible double-peaked solutions as well as the single- and triple- peaked solutions. This rotation period agrees with one of five possible solutions found in post-perihelion data from 1994 by Mueller and Ferrin (1996, Icarus, 123, 463-477), and unambiguously eliminates their remaining four solutions. We applied our same techniques to published lightcurves from 1988 which were obtained at an equivalent orbital position and viewing geometry as in 1999. We found a rotation period of 8.932 +/- 0.001 hr in 1988, consistent with the findings of previous authors and incompatible with our 1999 solution. This reveals that Tempel 2 spun-down by ~32 s between 1988 and 1999 (two intervening perihelion passages). If the spin-down is due to a systematic torque, then the rotation period prior to perihelion during the 2010 apparition is expected to be an additional 32 s longer than in 1999.Comment: Accepted by The Astronomical Journal; 22 pages of text, 3 tables, 6 figure