Plane Fitting(平面拟合)研究综述
Plane Fitting 平面拟合 - Afterwards, plane fitting is applied to the over-segmented supervoxels and seeds for region growing are selected with respect to the fitting residuals. [1] The results verified that the proposed method can achieve robust, dense reconstruction with depth accuracy at 20 μm while the root-mean-square error (RMSE) of plane fitting up to 43 μm. [2] Secondly, the external 3D point cloud environment is reconstructed in real time through the binocular camera, the pre-landing plane is found by plane fitting, and the 3D information is mapped to the 2D plane to extract the plane mask, and finally the random forest is used to determine the landing point. [3] We use the method of plane fitting to calculate the adjustment value of every panel’s corner. [4] Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. [5] On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. [6] To minimize the error of levelness deviation of two planes which caused by small size of the electronic level meters and roughness of the surface of large planes, methods based on least square fitting and plane fitting are proposed. [7] We also design a plane fitting-based algorithm for 3D point outliers removal to improve the 3D model quality. [8] 4D flow CMR aoPWV was calculated: using velocity curves at two locations, namely ascending aorta (AAo) and distal descending aorta (DAo) aorta (S1, 2D-like strategy), or using all velocity curves along the entire aortic centreline (3D-like strategies) with iterative transit time (TT) estimates (S2) or a plane fitting of velocity curves systolic upslope (S3). [9] Then, 3D label assignment is performed for occlusion outliers and normal-based plane fitting is conducted for mismatch outliers to refine the disparities of the outliers and to achieve an accurate stereo matching result. [10] To evaluate the system, five different objects were tested under four criteria including plane fitting, structural resolution test, scale resolving test and comparing with a reference 3D model obtained with a commercial accurate laser scanner known as GOM ATOS Compact laser scanner. [11] Then, a planarity-based extraction is conducted to segments, and only the planar segments, as well as their neighborhoods, are selected as candidates for the plane fitting. [12] Two-dimensional (2D) chess corners are reprojected into 3D space for plane fitting. [13] However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. [14] For a specific building, it is approximately treated as a plane object, and its height is assumed known to solve the range and parameters for plane fitting. [15] The approach is based on the RANSAC scheme of plane fitting. [16] We proposed the plane segmentation and fitting framework, which comprises of four steps: coordinate transformation, filtering, coarse segmentation, fine segmentation, plane fitting. [17] The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8. [18] We also design a plane fitting-based algorithm for 3D point outliers removal to improve the 3D model quality. [19] Thirdly, a set of all possible disparity planes are extracted and then plane fitting and neighboring segment merging are performed. [20] In this study, a filtering method was developed based on plane fitting by differential evolution algorithm to filter noisy point cloud data. [21] The approaches can be developed by applying different ideas: regularity of cluster boundary, plane fitting, radiometric data and also in geometric attribute derived from LiDAR. [22] The numerical examples of plane fitting and yaw computation show, that the MDB is also in the GHM an appropriate measure to analyze the ability of an implemented least-squares algorithm to detect if outliers are present. [23] In order to calculate the axis of rotation, the plane fitting and spatial arc fitting are used. [24] By sphere fitting, plane fitting, and point projection, the scattered point cloud data are combined together to obtain initial direction vectors. [25]然后，将平面拟合应用于过度分割的超体素，并根据拟合残差选择用于区域生长的种子。 [1] 结果验证了所提出的方法可以实现稳健、密集的重建，深度精度为 20 μm，而平面拟合的均方根误差 (RMSE) 高达 43 μm。 [2] 其次，通过双目摄像头实时重建外部3D点云环境，通过平面拟合找到着陆前平面，将3D信息映射到2D平面提取平面mask，最后得到随机森林用于确定着陆点。 [3] 我们使用平面拟合的方法来计算每个面板角的调整值。 [4] 典型的方法依赖于平面拟合或局部几何特征，但在倾斜地形或稀疏数据的情况下，它们的性能会降低。 [5] 在实现层面，我们仔细研究了激光雷达的物理测量原理，提出了一种基于点云体素切割和平面拟合的高效、准确的激光雷达边缘提取方法。 [6] 为尽量减少由于电子水平仪尺寸小和大平面表面粗糙造成的两个平面水平度偏差的误差，提出了基于最小二乘拟合和平面拟合的方法。 [7] 我们还设计了一种基于平面拟合的算法来去除 3D 点异常值，以提高 3D 模型质量。 [8] 计算 4D 血流 CMR aoPWV：使用两个位置的速度曲线，即升主动脉 (AAo) 和远端降主动脉 (DAo) 主动脉（S1，类似 2D 的策略），或使用沿整个主动脉中心线的所有速度曲线（3D-类似策略）具有迭代通过时间（TT）估计（S2）或速度曲线收缩上坡（S3）的平面拟合。 [9] 然后，对遮挡异常值进行3D标签分配，并对失配异常值进行基于法线的平面拟合，以细化异常值的视差并获得准确的立体匹配结果。 [10] 为了评估该系统，五个不同的对象在四个标准下进行了测试，包括平面拟合、结构分辨率测试、比例分辨率测试，并与使用称为 GOM ATOS 紧凑型激光扫描仪的商用精确激光扫描仪获得的参考 3D 模型进行比较。 [11] 然后，对线段进行基于平面度的提取，仅选择平面线段及其邻域作为平面拟合的候选对象。 [12] 将二维 (2D) 棋角重新投影到 3D 空间以进行平面拟合。 [13] 然而，平面拟合产生的方向估计可能是高度不确定的，特别是当观察到的数据近似共线或感兴趣的结构包含不同方向的片段时。 [14] 对于特定的建筑物，它被近似地视为一个平面对象，并假设它的高度是已知的，以解决平面拟合的范围和参数。 [15] 该方法基于平面拟合的 RANSAC 方案。 [16] 我们提出了平面分割与拟合框架，它包括四个步骤：坐标变换、滤波、粗分割、精细分割、平面拟合。 [17] LiDAR点云质量也通过平面拟合进行评估，结果表明LiDAR点云质量提高了8倍。 [18] 我们还设计了一种基于平面拟合的算法来去除 3D 点异常值，以提高 3D 模型质量。 [19] 第三，提取一组所有可能的视差平面，然后进行平面拟合和相邻段合并。 [20] 本研究提出了一种基于平面拟合的差分进化算法来过滤噪声点云数据的过滤方法。 [21] 这些方法可以通过应用不同的想法来开发：集群边界的规律性、平面拟合、辐射数据以及从 LiDAR 派生的几何属性。 [22] 平面拟合和偏航计算的数值示例表明，MDB 也是 GHM 中分析实施的最小二乘算法检测异常值是否存在的能力的适当措施。 [23] 为了计算旋转轴，使用平面拟合和空间弧拟合。 [24] 通过球面拟合、平面拟合和点投影，将分散的点云数据组合在一起，得到初始方向向量。 [25]
Local Plane Fitting
The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. [1] It is a point cloud postprocessing method, which exploits secondary denoising and local plane fitting in a projected coordinate system. [2] After finding an initial disparity map, we find local and global plane hypotheses from the disparity map through segmentation-based local plane fitting, agglomerative hierarchical clustering, and energy-based multi-model fitting techniques. [3] For each matched line, we perform a local discontinuity analysis and propose an intensity-based weighting method for a local plane fitting using iteratively solved weighted least squares adjustment, such that straightness of the object's edges (e. [4]改进的方法包括三个阶段：（1）使用改进的局部平面拟合方法估计点云的法线； (2) 使用改进的最小生成树方法重定向点云的法线； (3) 使用隐函数构造地质模型。 [1] 它是一种点云后处理方法，在投影坐标系中利用二次去噪和局部平面拟合。 [2] 在找到初始视差图后，我们通过基于分割的局部平面拟合、凝聚层次聚类和基于能量的多模型拟合技术从视差图中找到局部和全局平面假设。 [3] 对于每条匹配的线，我们执行局部不连续性分析，并提出一种基于强度的加权方法，用于使用迭代求解加权最小二乘调整的局部平面拟合，使得对象边缘的直线度（例如 [4]
Square Plane Fitting 方平面接头
Planes are fitted to all clusters based on Least Square Plane Fitting (LSPF) method and line segments from their intersection are identified, highlighted and collected as feature lines i. [1] By leveraging the larger radius symbols which correspond to a non-trivial proportion of the PS signal, a more accurate estimation of the normal vector is obtained for PolDemux by least squares plane fitting with these higher signal-to-noise ratio (SNR) symbols. [2] And the error of plane calculated by the NURBS surface fitting is smallest by comparing with the least-squares plane fitting, Delaunay triangulation and polygon mesh method. [3]基于最小二乘平面拟合 (LSPF) 方法将平面拟合到所有集群，并且从它们的交点处识别、突出显示并收集作为特征线 i 的线段。 [1] 通过利用与 PS 信号的重要比例相对应的较大半径符号，通过与这些较高信噪比 (SNR) 符号拟合的最小二乘平面，为 PolDemux 获得法向矢量的更准确估计。 [2] 与最小二乘平面拟合、Delaunay三角剖分和多边形网格法相比，NURBS曲面拟合计算出的平面误差最小。 [3]
Ground Plane Fitting 地平面配件
This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. [1] Finally, Region-wise Ground Plane Fitting (R-GPF) is adopted to distinguish static points from dynamic points within the candidate bins that potentially contain dynamic points. [2] In this paper, a ground segmentation method based on ground plane fitting is discussed, and a human detection method based on point cloud clustering and an enhanced characteristic using reflection intensity is carried out. [3]随后是区域级地平面拟合，执行该拟合以估计每个箱的部分地面。 [1] 最后，采用 Region-wise Ground Plane Fitting (R-GPF) 来区分候选 bin 中可能包含动态点的静态点和动态点。 [2] 本文讨论了一种基于地平面拟合的地面分割方法，并提出了一种基于点云聚类和反射强度增强特征的人体检测方法。 [3]
3d Plane Fitting
We proposes an efficient semi-global matching method that disparity search range is reduced based on 3D plane fitting. [1] Finally, a 3D plane fitting is applied to shape the rooftop using lidar points inside building primitive. [2]我们提出了一种高效的半全局匹配方法，基于 3D 平面拟合缩小视差搜索范围。 [1] 最后，使用建筑物图元内的激光雷达点应用 3D 平面拟合来塑造屋顶。 [2]
plane fitting method 平面拟合法
Using the plane fitting method to analyse the accuracy, a qualitative analysis conclusion is drawn that as t he distance increases, the angle of incidence increases, which will cause the measurement accuracy of the ground 3D laser scanner to decrease. [1] In this paper PC produced from different methods was filtering with Shepard Inverse Distance Weighting method, Gaussian Filtering method, Single Value Decomposition Based Plane Fitting method and Optimization Based Plane Fitting method. [2] A plane fitting method is proposed to separate the two angle motions from a single phase map. [3] The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. [4] A plane fitting method was used to obtain the material parameters of two, six-sided Paul–Mohr–Coulomb failure surfaces, which capture the strength characteristics of the sandstone over a range of mean stress. [5]采用平面拟合法分析精度，得出定性分析结论，即随着距离的增加，入射角增大，会导致地面3D激光扫描仪的测量精度降低。 [1] 本文采用Shepard逆距离加权法、高斯滤波法、基于单值分解的平面拟合法和基于优化的平面拟合法对不同方法产生的PC进行滤波。 [2] 提出了一种平面拟合方法，将两个角度运动从单个相位图中分离出来。 [3] 改进的方法包括三个阶段：（1）使用改进的局部平面拟合方法估计点云的法线； (2) 使用改进的最小生成树方法重定向点云的法线； (3) 使用隐函数构造地质模型。 [4] 使用平面拟合方法获得了两个六面 Paul-Mohr-Coulomb 破坏面的材料参数，这些参数捕捉了砂岩在一定平均应力范围内的强度特征。 [5]
plane fitting algorithm 平面拟合算法
In addition, a probabilistic plane fitting algorithm is proposed to fit a plane model to the noisy 3-D points. [1] In order to reduce the illumination errors introduced by plane fitting, we propose several strategies, including an illumination-guided plane fitting algorithm, a normal correction factor and a virtual light sources reuse strategy. [2] In addition, a plane fitting algorithm using the homogeneous transformation was developed to compute plane normal vectors to help improve attitude estimation accuracy. [3]此外，提出了一种概率平面拟合算法，将平面模型拟合到有噪声的 3-D 点。 [1] 为了减少平面拟合引入的光照误差，我们提出了几种策略，包括光照引导的平面拟合算法、法线校正因子和虚拟光源重用策略。 [2] 此外，还开发了一种使用齐次变换的平面拟合算法来计算平面法向量，以帮助提高姿态估计精度。 [3]
plane fitting accuracy
In addition, the plane fitting accuracy of lateral point clouds was improved 4. [1] The first novelty is the dynamic region size adjusting algorithm that can reduce the number of regions to be clustered and improve the plane fitting accuracy. [2]此外，提高了横向点云的平面拟合精度 4. [1] 第一个新颖之处是动态区域大小调整算法，可以减少要聚类的区域数量，提高平面拟合精度。 [2]