Local quality assessment of point clouds for indoor mobile mapping

The quality of point clouds obtained by RGB-D camera-based indoor mobile mapping can be limited by local degradation because of complex scenarios such as sensor characteristics, partial occlusions, cluttered backgrounds, and complex illumination conditions. This paper presents a machine learning framework to assess the local quality of indoor mobile mapping point cloud data. In our proposed framework, a point cloud dataset with multiple kinds of quality problems is first created by manual annotation and degradation simulation. Then, feature extraction methods based on 3D patches are treated as operating units to conduct quality assessment in local regions. Also, a feature selection algorithm is deployed to obtain the essential components of feature sets that are used to effectively represent local degradation. Finally, a semi-supervised method is introduced to classify quality types of point clouds. Comparative experiments demonstrate that the proposed framework obtained promising quality assessment results with limited labeled data and a large amount of unlabeled data. A point cloud dataset with multiple kinds of quality problems is created.The main causes of point cloud data degradation in indoor mobile mapping is analyzed.A novel semi-supervised framework is proposed for quality assessment of indoor mobile mapping point cloud data.

[1]  Kourosh Khoshelham,et al.  Accuracy analysis of kinect depth data , 2012 .

[2]  Lei Zhang,et al.  Learning without Human Scores for Blind Image Quality Assessment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Thorsten Joachims,et al.  Contextually guided semantic labeling and search for three-dimensional point clouds , 2013, Int. J. Robotics Res..

[5]  Avideh Zakhor,et al.  Indoor localization and visualization using a human-operated backpack system , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[6]  Ming Cheng,et al.  Combinative hypergraph learning for semi-supervised image classification , 2015, Neurocomputing.

[7]  Ashish Kapoor,et al.  Blind Image Quality Assessment Using Semi-supervised Rectifier Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Yuan Yan Tang,et al.  High-Order Distance-Based Multiview Stochastic Learning in Image Classification , 2014, IEEE Transactions on Cybernetics.

[9]  Y. Rui,et al.  Learning to Rank Using User Clicks and Visual Features for Image Retrieval , 2015, IEEE Transactions on Cybernetics.

[10]  Meng Wang,et al.  Visual Classification by ℓ1-Hypergraph Modeling , 2015, IEEE Trans. Knowl. Data Eng..

[11]  Xiaoqiang Lu,et al.  Image quality assessment: A sparse learning way , 2015, Neurocomputing.

[12]  Lai-Man Po,et al.  No-reference image quality assessment with shearlet transform and deep neural networks , 2015, Neurocomputing.

[13]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[14]  Jun Yu,et al.  High-level attributes modeling for indoor scenes classification , 2013, Neurocomputing.

[15]  Thorsten Joachims,et al.  Semantic Labeling of 3D Point Clouds for Indoor Scenes , 2011, NIPS.

[16]  Tingting Wang,et al.  No reference image quality assessment using sparse feature representation in two dimensions spatial correlation , 2016, Neurocomputing.

[17]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  Antônio de Pádua Braga,et al.  SVM-KM: speeding SVMs learning with a priori cluster selection and k-means , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[19]  Avideh Zakhor,et al.  Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System , 2013, Remote. Sens..

[20]  Burcu Akinci,et al.  Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data , 2013 .

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

[22]  Witold R. Rudnicki,et al.  Feature Selection with the Boruta Package , 2010 .

[23]  Michael Bosse,et al.  Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping , 2012, IEEE Transactions on Robotics.

[24]  Xuelong Li,et al.  Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model , 2015, IEEE Transactions on Cybernetics.

[25]  Nico Blodow,et al.  Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Jun Yu,et al.  Click Prediction for Web Image Reranking Using Multimodal Sparse Coding , 2014, IEEE Transactions on Image Processing.

[27]  Meng Wang,et al.  Unified Video Annotation via Multigraph Learning , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[29]  Ayman F. Habib,et al.  Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Yong Yang,et al.  Quality Evaluation of Spatial Point-Cloud Data Collected by Vehicle-Borne Laser Scanner , 2008, 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing.

[31]  Antanas Verikas,et al.  Mining data with random forests: A survey and results of new tests , 2011, Pattern Recognit..

[32]  Zhi-Hua Zhou,et al.  Towards Making Unlabeled Data Never Hurt , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  George Vosselman,et al.  Quality analysis on 3D building models reconstructed from airborne laser scanning data , 2011 .

[34]  Kellie J. Archer,et al.  Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..

[35]  Meng Wang,et al.  Adaptive Hypergraph Learning and its Application in Image Classification , 2012, IEEE Transactions on Image Processing.

[36]  Martial Hebert,et al.  Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[38]  Dan Xia,et al.  Handling occlusions in augmented reality based on 3D reconstruction method , 2015, Neurocomputing.

[39]  Ling Qin,et al.  Three-Dimensional Indoor Mobile Mapping With Fusion of Two-Dimensional Laser Scanner and RGB-D Camera Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[40]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[41]  Dinesh Manocha,et al.  3D Reconstruction in the presence of glasses by acoustic and stereo fusion , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).