GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

[1]  Friedrich Fraundorfer,et al.  Automatic Alignment of Indoor and Outdoor Building Models Using 3D Line Segments , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Sergio A. Velastin,et al.  A Review of Computer Vision Techniques for the Analysis of Urban Traffic , 2011, IEEE Transactions on Intelligent Transportation Systems.

[3]  Uwe Stilla,et al.  Voxel Based Segmentation of Large Airborne Topobathymetric LIDAR Data , 2017 .

[4]  George Vosselman,et al.  Contextual segment-based classification of airborne laser scanner data , 2017 .

[5]  Cheng Wang,et al.  Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data , 2016 .

[6]  Bisheng Yang,et al.  An efficient global energy optimization approach for robust 3D plane segmentation of point clouds , 2018 .

[7]  Cheng Wang,et al.  Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[9]  Uwe Stilla,et al.  VOXEL- AND GRAPH-BASED POINT CLOUD SEGMENTATION OF 3D SCENES USING PERCEPTUAL GROUPING LAWS , 2017 .

[10]  Yusheng Xu,et al.  Geometric Primitive Extraction From Point Clouds of Construction Sites Using VGS , 2017, IEEE Geoscience and Remote Sensing Letters.

[11]  Xiao Xiang Zhu,et al.  Data Fusion and Remote Sensing: An ever-growing relationship , 2016, IEEE Geoscience and Remote Sensing Magazine.

[12]  Marc Pollefeys,et al.  Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark , 2017, ArXiv.

[13]  Uwe Stilla,et al.  An Approach to Extract Moving Objects from Mls Data Using a Volumetric Background Representation , 2017 .

[14]  Yusheng Xu,et al.  Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model , 2018, Pattern Recognit. Lett..

[15]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[16]  Stefan Hinz,et al.  Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis , 2011 .

[17]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[18]  Bisheng Yang,et al.  Automated registration of dense terrestrial laser-scanning point clouds using curves , 2014 .

[19]  David Belton,et al.  Comparative Study of Automatic Plane Fitting Registration for Mls Sparse Point Clouds with Different Plane Segmentation Methods , 2017 .

[20]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[21]  J. Niemeyer,et al.  Contextual classification of lidar data and building object detection in urban areas , 2014 .

[22]  Michela Bertolotto,et al.  Octree-based region growing for point cloud segmentation , 2015 .

[23]  Jaewook Jung,et al.  Results of the ISPRS benchmark on urban object detection and 3D building reconstruction , 2014 .

[24]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[25]  Horst Bischof,et al.  Efficient 3D scene abstraction using line segments , 2017, Comput. Vis. Image Underst..

[26]  Jan Dirk Wegner,et al.  Contour Detection in Unstructured 3D Point Clouds , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Uwe Stilla,et al.  AUTOMATED COARSE REGISTRATION OF POINT CLOUDS IN 3D URBAN SCENESUSING VOXEL BASED PLANE CONSTRAINT , 2017 .

[28]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[29]  Xuming Ge,et al.  Surface-based matching of 3D point clouds with variable coordinates in source and target system , 2016 .

[30]  Daniel Cohen-Or,et al.  4-points congruent sets for robust pairwise surface registration , 2008, ACM Trans. Graph..

[31]  Martin Simonovsky,et al.  Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  Konrad Schindler,et al.  Keypoint-based 4-Points Congruent Sets – Automated marker-less registration of laser scans , 2014 .

[33]  Hans-Peter Seidel,et al.  Exploiting global connectivity constraints for reconstruction of 3D line segments from images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.