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

3D Reconstruction Using Multiple View Stereo and a Brief Introduction to Kinect

2014 ◽  
pp. 15-37
Author(s):  
Brojeshwar Bhowmick
Keyword(s):  
3D Reconstruction ◽  
Projective Geometry ◽  
Point Cloud ◽  
Microsoft Kinect ◽  
Sparse Reconstruction ◽  
Basic Properties ◽  
Multiple View ◽  
The Subject ◽  
Dense Reconstruction ◽  
3D Information

This chapter deals with the methodology of 3D reconstruction, both sparse and dense. The basic properties of the projective geometry and the camera models are introduced to understand the preliminaries about the subject. A more detail can be found in the book (Hartley & Zisserman, 2000). The sparse reconstruction deals with reconstructing 3D points for few image points. There are gaps in the reconstructed 3D points. Dense reconstruction tries to fill up gaps and make the density of the reconstruction higher. Estimation of correspondences is an integral part of multiview reconstruction and the author will discuss the point correspondences among images here. Finally the author will introduce the Microsoft Kinect, a divice which directly capture 3D information in realtime, and will show how to enhance the Kinect point cloud using vision framework.

Analysis of Aero-engine Blade Surface Laser Point Cloud 3D Reconstruction Error

2018 ◽  
Vol 47 (5) ◽  
pp. 512002
Author(s):  
孙彬 SUN Bin ◽  
杜虎兵 DU Hu-bing ◽  
王建华 WANG Jian-hua ◽  
李兵 LI Bing
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Reconstruction Error ◽  
Blade Surface ◽  
Aero Engine ◽  
Engine Blade ◽  
Surface Laser

3D Reconstruction Approach for Outdoor Scene Based on Multiple Point Cloud Fusion

2019 ◽  
Vol 47 (10) ◽  
pp. 1761-1772 ◽  
Author(s):  
Hui Chen ◽  
Yan Feng ◽  
Jian Yang ◽  
Chenggang Cui
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Multiple Point

scholarly journals Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 187
Author(s):  
Yuxia Bian ◽  
Xuejun Liu ◽  
Meizhen Wang ◽  
Hongji Liu ◽  
Shuhong Fang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Point Distribution ◽  
Multiple Images ◽  
Quantification Method ◽  
Real Scene ◽  
Direct Data ◽  
The Relationship ◽  
Overlapping Region

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan ( H D O P × n 5 − 1 ) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution.

scholarly journals Extreme Feature Regions Detection and Accurate Quality Assessment for Point-Cloud 3D Reconstruction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37757-37769 ◽  
Author(s):  
Yunbo Rao ◽  
Baijiang Fan ◽  
Qifei Wang ◽  
Jiansu Pu ◽  
Xun Luo ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Quality Assessment ◽  
Point Cloud

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 Deformation for Single Image 3d Reconstruction

2019 ◽  
Author(s):  
Seonghwa Choi ◽  
Anh-Duc Nguyen ◽  
Jinwoo Kim ◽  
Sewoong Ahn ◽  
Sanghoon Lee
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Single Image

Acquisition of Point Cloud Data in 3D Reconstruction

2014 ◽  
Vol 536-537 ◽  
pp. 213-217
Author(s):  
Meng Qiang Zhu ◽  
Jie Yang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Point Cloud ◽  
Corner Detection ◽  
Projection Mapping ◽  
Cloud Data ◽  
3D Space ◽  
Dense Point ◽  
Left And Right ◽  
Object Depth

This paper takes the following measures to solve the problem of 3D reconstruction. Camera calibration is based on chessboard, taking several different attitude images. Use corner point coordinates by corner detection to process camera calibration. The calibration result is important to be used to correct the distorted image. Next, the left and right images should be matched to find out the object surface points’ imaging position respectively so that the object depth can be calculated by triangulation. According to the inverse process of projection mapping, we can project the object depth and disparity information into 3D space. As a result, we can obtain dense point cloud, which is ready for 3D reconstruction.

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