Plane Fitting Algorithm Research Articles

COMBINING 3D VOLUME AND MESH MODELS FOR REPRESENTING COMPLICATED HERITAGE BUILDINGS

Abstract. This study developed a simple but effective strategy to combine 3D volume and mesh models for representing complicated heritage buildings and structures. The idea is to seamlessly integrate 3D parametric or polyhedral models and mesh-based digital surfaces to generate a hybrid 3D model that can take advantages of both modeling methods. The proposed hybrid model generation framework is separated into three phases. Firstly, after acquiring or generating 3D point clouds of the target, these 3D points are partitioned into different groups. Secondly, a parametric or polyhedral model of each group is generated based on plane and surface fitting algorithms to represent the basic structure of that region. A “bare-bones” model of the target can subsequently be constructed by connecting all 3D volume element models. In the third phase, the constructed bare-bones model is used as a mask to remove points enclosed by the bare-bones model from the original point clouds. The remaining points are then connected to form 3D surface mesh patches. The boundary points of each surface patch are identified and these boundary points are projected onto the surfaces of the bare-bones model. Finally, new meshes are created to connect the projected points and original mesh boundaries to integrate the mesh surfaces with the 3D volume model. The proposed method was applied to an open-source point cloud data set and point clouds of a local historical structure. Preliminary results indicated that the reconstructed hybrid models using the proposed method can retain both fundamental 3D volume characteristics and accurate geometric appearance with fine details. The reconstructed hybrid models can also be used to represent targets in different levels of detail according to user and system requirements in different applications.

Motion vector recovery for video error concealment based on the plane fitting

In a wireless transmission, the compressed video stream can result in lower quality of image reconstruction with the motion vector mismatch. A motion vector recovery for video error concealment based on the plane fitting algorithm is proposed. According to the characteristics of the H.264 video standard, the adjacent macro blocks and the multi reference frame have the correlation. The motion vector which surrounds the lost macro-block in each divided block is projected by one plane which adopts one point to present the MV changing tendency in small regions. This algorithm reconstructs the motion vector of the damaged macro-block by the fitting plane algorithm based on the improved boundary matching algorithm, and then the optimal motion vector is selected to reconstruct the damaged images. This algorithm not only avoids the block effects that caused by the original algorithm, but also in different probability of RTP packet losing, the peak signal to noise ratio of the algorithm has improved 0.3 ~ 2.5 dB compared to the adaptive intra-frame error concealment algorithm .

3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

Artificial perception, in the context of autonomous driving, is the process by which an intelligent system translates sensory data into an effective model of the environment surrounding a vehicle. In this paper, and considering data from a 3D-LIDAR mounted onboard an intelligent vehicle, a 3D perception system based on voxels and planes is proposed for ground modeling and obstacle detection in urban environments. The system, which incorporates time-dependent data, is composed of two main modules: (i) an effective ground surface estimation using a piecewise plane fitting algorithm and RANSAC-method, and (ii) a voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information. This perception system has direct application in safety systems for intelligent vehicles, particularly in collision avoidance and vulnerable road users detection, namely pedestrians and cyclists. Experiments, using point-cloud data from a Velodyne LIDAR and localization data from an Inertial Navigation System were conducted for both a quantitative and a qualitative assessment of the static/moving obstacle detection module and for the surface estimation approach. Reported results, from experiments using the KITTI database, demonstrate the applicability and efficiency of the proposed approach in urban scenarios.

Algebraic method to speed up robust algorithms: example of laser-scanned point clouds

Surface reconstruction from point clouds generated by laser scanning technology has become a fundamental task in many fields of geosciences, such as robotics, computer vision, digital photogrammetry, computational geometry, digital building modelling, forest planning and operational activities. Point clouds produced by laser scanning, however, are limited due to the occurrence of occlusions, multiple reflectance and noise, and off-surface points (outliers), thus necessitating the need for robust fitting techniques. In this contribution, a fast, non-iterative and data invariant algebraic algorithm with constant O(1) complexity that fits planes to point clouds in the total least squares sense using Gaussian-type error distribution is proposed. The maximum likelihood estimator method is used, resulting in a multivariate polynomial system that is solved in an algebraic way. It is shown that for plane fitting when datasets are affected heavily by outliers, the proposed algebraic method can be embedded into the framework of robust methods like the Danish or the RANdom SAmple Consensus methods and computed in parallel to provide rigorous algebraic fitting with significantly reduced running times. Compared to the embedded traditional singular value decomposition and principal component analysis approaches, the performance of the proposed algebraic algorithm demonstrated its efficiency on both synthetic data and real laser-scanned measurements. The evaluation of a symbolic algebraic formula is practically independent of the values of its coefficients; however, the computation of the coefficients depends on the complexity of the data. Since the main advantage of the symbolic solution is its non-requirement of numerical iteration, the data complexity will have weak influence on the speed-up. The novelty of the proposed method is the use of algebraic technique in a robust plane fitting algorithm that could be applied to remote sensing data analysis/delineation/classification. In general, the method could be applied to most plane fitting problems in the geoscience field.

AUTOMATIC BUILDING EXTRACTION AND ROOF RECONSTRUCTION IN 3K IMAGERY BASED ON LINE SEGMENTS

We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo-imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.

AUTOMATIC BUILDING EXTRACTION AND ROOF RECONSTRUCTION IN 3K IMAGERY BASED ON LINE SEGMENTS

Abstract. We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo-imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.

Development of Mobile Mapping System for 3D Road Asset Inventory.

Asset Management is an important component of an infrastructure project. A significant cost is involved in maintaining and updating the asset information. Data collection is the most time-consuming task in the development of an asset management system. In order to reduce the time and cost involved in data collection, this paper proposes a low cost Mobile Mapping System using an equipped laser scanner and cameras. First, the feasibility of low cost sensors for 3D asset inventory is discussed by deriving appropriate sensor models. Then, through calibration procedures, respective alignments of the laser scanner, cameras, Inertial Measurement Unit and GPS (Global Positioning System) antenna are determined. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. By using derived sensor models, geo-referenced images and 3D point clouds are derived. After validating the quality of the derived data, the paper provides a framework to extract road assets both automatically and manually using techniques implementing RANSAC plane fitting and edge extraction algorithms. Then the scope of such extraction techniques along with a sample GIS (Geographic Information System) database structure for unified 3D asset inventory are discussed.

光机集成仿真前处理中点云边界检测技术

To resolve the data processing problem of point cloud for optical surface in general interface of optical;mechanical integrated simulation, the surface data processing method based on point cloud boundary detection was presented. First, the surface data preprocessing method in integrated simulation was discussed and effect of boundary detection in surface preprocessing data was also referred. Then, boundary detection algorithm referring to several techniques was researched including the data organization of point cloud, the small tangential plane fitting algorithm of the K;nearest neighbor node and the judgment of boundary node. Based on the research of point cloud boundary detection algorithm, the primary data structure and the program realization was discussed and by the extraction of demonstrated surface point cloud, the algorithm of boundary detection was proved to be right and effective. The algorithm of boundary detection provides the new technical reference for data process of optical surface. © 2016, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.

스테레오 비전를 이용한 무인기 고도 및 자세 추정기법

This paper presents the implementation of altitude and attitude measurement algorithm using stereo camera for an unmanned aerial vehicle (UAV). Depth images are generated by calibrating the stereo cameras, and converted into 3D point cloud data. By applying a plane fitting algorithm to the resultant point cloud, altitude from ground level, and roll and pitch angles are extracted. To verify the performance, experimental results are provided by comparing with those of the motion caption system.

Robust plane fitting algorithm for landing hazard detection

An innovative three-dimensional hazard detection algorithm is proposed for a planetary safe landing mission. Typically, plane fitting is carried out to approximate the surface of the planetary terrain. Current state-of-the-art plane estimation methods with a low breakdown point do not meet the requirements of future pinpoint landing missions. In this paper, a novel robust scale estimator called Preprocessing and Double Iterative Median Scale Estimator (PDIMSE) is proposed, which can achieve a breakdown point of more than 50%. It can be applied to future Mars landing missions.

A stereo matching approach based on particle filters and scattered control landmarks

In robot localization, particle filtering can estimate the position of a robot in a known environment with the help of sensor data. In this paper, we present an approach based on particle filtering, for accurate stereo matching. The proposed method consists of three parts. First, we utilize multiple disparity maps in order to acquire a very distinctive set of features called landmarks, and then we use segmentation as a grouping technique. Secondly, we apply scan line particle filtering using the corresponding landmarks as a virtual sensor data to estimate the best disparity value. Lastly, we reduce the computational redundancy of particle filtering in our stereo correspondence with a Markov chain model, given the previous scan line values. More precisely, we assist particle filtering convergence by adding a proportional weight in the predicted disparity value estimated by Markov chains. In addition to this, we optimize our results by applying a plane fitting algorithm along with a histogram technique to refine any outliers. This work provides new insights into stereo matching methodologies by taking advantage of global geometrical and spatial information from distinctive landmarks. Experimental results show that our approach is capable of providing high-quality disparity maps comparable to other well-known contemporary techniques.

Automatic evaluation of machining allowance of precision castings based on plane features from 3D point cloud

A novel automatic precision casting machining allowance evaluation approach, which is accomplished by a two-step rough-precise point cloud registration based on plane features extracted from the two point clouds (i.e. the measured precision casting point cloud and the point cloud discretized from the CAD model), is proposed in this paper. Firstly, the two point clouds are registered roughly by PCA algorithm. Secondly, an improved plane fitting and merging algorithm is proposed to extract the plane features from both the two point clouds. The extracted plane features are matched by searching the nearest plane feature description vector. The rotation matrix for the precise registration can then be derived by registering the normal vectors of the matched plane features. Finally, the machining allowance at each point is obtained by calculating the distance between the corresponding points along the normal direction. The experiment on precision casting machining allowance evaluation is given to show the performance of the proposed approach.

Identification of pulmonary fissures using a piecewise plane fitting algorithm

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created “reference standard” and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.

An Improved Plane Fitting based Filtering Algorithm for Airborne LiDAR Data

Due to the strengths and weaknesses of the existing filtering methods,an improved plane fitting algorithm is presented for filtering of airborne LiDAR data.Firstly,resampled LiDAR data were segmented using region growing method,and then the largest connected region was selected as the initial ground area.Secondly,non-ground points in the initial ground area were removed by a slope threshold.Finally,the ground points were interpolated using Kriging method and the filtering results were gained.The dataset provided by International Society for Photogrammetry and Remote Sensing(ISPRS) were selected as the test data.The influences of different slope thresholds on the filtering results were given after filtering experiments.The result shows that the optimal filtering effect is the best when the slope threshold sets to 0.5.The overall error rate can be only 4.29%.Contrast to the traditional filtering method based on plane fitting,the proposed method is more simple and practical.

Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.