Low-complexity adaptive radius outlier removal filter based on PCA for lidar point cloud denoising

2021 ◽  
Vol 60 (20) ◽  
pp. E1
Author(s):  
Yao Duan ◽  
Chuanchuan Yang ◽  
Hongbin Li
Keyword(s):  
Point Cloud ◽  
Low Complexity ◽  
Outlier Removal ◽  
Adaptive Radius

scholarly journals Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances

2019 ◽  
Vol 8 (4) ◽  
pp. 178 ◽  
Author(s):  
Richard Boerner ◽  
Yusheng Xu ◽  
Ramona Baran ◽  
Frank Steinbacher ◽  
Ludwig Hoegner ◽  
...  
Keyword(s):  
Rural Areas ◽  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Target Point ◽  
Outlier Removal ◽  
Least Squares Optimization ◽  
Rotation Parameters ◽  
Airborne Sensors ◽  
Fine Registration

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.

Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction

2016 ◽  
Author(s):  
Katja Wolff ◽  
Changil Kim ◽  
Henning Zimmer ◽  
Christopher Schroers ◽  
Mario Botsch ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Outlier Removal

scholarly journals Superimposing holograms on real world objects using HoloLens 2 and its depth camera

2021 ◽  
Vol 7 (1) ◽  
pp. 111-115
Author(s):  
Felix von Haxthausen ◽  
Yenjung Chen ◽  
Floris Ernst
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Initial Step ◽  
Depth Camera ◽  
Visual Guidance ◽  
Outlier Removal ◽  
Surgical Interventions ◽  
Registration Algorithm ◽  
Spatial Registration ◽  
Initial Placement

Abstract Augmented Reality glasses such as HoloLens 2 may provide visual guidance during surgical interventions. To superimpose the holograms on real world objects (RWO), for instance a patient, spatial registration is required. In this work, we propose an approach to automatically register a hologram to the according RWO. To this end, the framework utilizes the depth camera of HoloLens 2 to acquire the point cloud (PC) of the RWO. A novel and recently published PC registration algorithm allows to register the PC of the RWO and the hologram after a rough initial placement without any need for pre-processing or outlier removal. The approach is evaluated by measuring displacements between certain known positions of the hologram and the RWO. The first metric relies on measuring points using an optically tracked stylus while the second is based on visually perceived positions. The median displacements were 22.3 mm, 35.6 mm, and 13.3 mm for the x-, y-, and z-axes in the first metric and 8.1 mm, 4.3 mm, and 11.9 mm for the second metric. Even though the accuracy is not yet adequate for many surgical interventions, the framework provides an initial step for a convenient marker less registration of holograms to an RWO.

scholarly journals Triangulation Reconstruction for 3D Surface Based on Information Model

2016 ◽  
Vol 16 (5) ◽  
pp. 27-33
Author(s):  
Na Li ◽  
Jiquan Yang ◽  
Aiqing Guo ◽  
Yijian Liu ◽  
Hai Liu
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Low Complexity ◽  
Information Model ◽  
Landslide Monitoring ◽  
Reconstruction Method ◽  
Cloud Data ◽  
3D Surface ◽  
3D Information

Abstract The aim of this paper is to address the surface reconstruction from point cloud in reverser engineering. The data was acquired through a 3D scan device and was processed as point cloud data. The points in cloud were connected to build 3D surface. The points cloud was processed in four steps to get 3D information surface. First, the subtraction scheme was used to get cover boxes ended with the set of convex was found under the convergence rule. Secondly, the points in the box were projected to the directions which were close to the normal direction method. Thirdly the overlap was avoided by using convergence rule and inner subdivision rule. Finally the information model was used to reconstruction. The method was used in landslide monitoring of Three Gorges area for 3D surface reconstruction and monitoring. The reconstruction method obtains high precision and low complexity. It is effective for large scale monitoring.

scholarly journals Geometric Modeling of Rosa roxburghii Fruit Based on Three-Dimensional Point Cloud Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiping Xie ◽  
Yancheng Lang ◽  
Luqi Chen
Keyword(s):  
Geometric Modeling ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
3D Point Cloud ◽  
Outlier Removal ◽  
Actual Surface ◽  
Rosa Roxburghii

Fruit three-dimensional (3D) model is crucial to estimating its geometrical and mechanical properties and improving the level of fruit mechanical processing. Considering the complex geometrical features and the required model accuracy, this paper proposed a 3D point cloud reconstruction method for the Rosa roxburghii fruit based on a three-dimensional laser scanner, including 3D point cloud generation, point cloud registration, fruit thorns segmentation, and 3D reconstruction. The 3D laser scanner was used to obtain the original 3D point cloud data of the Rosa roxburghii fruit, and then the fruit thorns data were removed by the segmentation algorithm combining the statistical outlier removal and radius outlier removal. By analyzing the effects of five-point cloud simplification methods, the optimal simplification method was determined. The Poisson reconstruction algorithm, the screened Poisson reconstruction algorithm, the greedy projection triangulation algorithm, and the Delaunay triangulation algorithm were utilized to reconstruct the fruit model. The number of model vertices, the number of facets, and the relative volume error were used to determine the best reconstruction algorithm. The results indicated that this model can better reconstruct the actual surface of Rosa roxburghii fruit. The method provides a reference for the related application.

scholarly journals An efficient outlier removal method for scattered point cloud data

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0201280 ◽  
Author(s):  
Xiaojuan Ning ◽  
Fan Li ◽  
Ge Tian ◽  
Yinghui Wang
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Outlier Removal ◽  
Cloud Data ◽  
Removal Method
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