WEIGHTED MULTI-PROJECTION: 3D POINT CLOUD DENOISING WITH TANGENT PLANES

We present a novel algorithm for 3D point cloud denoising called weighted multi-projection. As a collection of 3D points sampled from surfaces of objects, a 3D point cloud is widely used in robotics, autonomous driving and augmented reality. Due to the physical limitations of 3D sensing devices, 3D point clouds are usually noisy, which influences subsequent computations. Compared to many previous denoising works, instead of directly smoothing the coordinates of 3D points, we use a two-fold smoothing. We first estimate a local tangent plane at each 3D point and then reconstruct each 3D point by weighted averaging of its projections on multiple tangent planes. We validate the empirical performance on the dataset of ShapeNetCore and show that weighted multi-projection outperforms its competitors in all nine classes.

[1]  Daniel Cohen-Or,et al.  Bilateral mesh denoising , 2003 .

[2]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Paolo Cignoni,et al.  MeshLab: an Open-Source Mesh Processing Tool , 2008, Eurographics Italian Chapter Conference.

[4]  Chen Feng,et al.  Fast Resampling of Three-Dimensional Point Clouds via Graphs , 2017, IEEE Transactions on Signal Processing.

[5]  Michael S. Brown,et al.  High quality depth map upsampling for 3D-TOF cameras , 2011, 2011 International Conference on Computer Vision.

[6]  Carlo de Franchis,et al.  The Bilateral Filter for Point Clouds , 2017, Image Process. Line.

[7]  Ling Shao,et al.  Enhanced Computer Vision With Microsoft Kinect Sensor: A Review , 2013, IEEE Transactions on Cybernetics.

[8]  Hao Chen,et al.  Denoising of point cloud data for computer-aided design, engineering, and manufacturing , 2018, Engineering with Computers.

[9]  Lei Gao,et al.  A review of algorithms for filtering the 3D point cloud , 2017, Signal Process. Image Commun..

[10]  Leonidas J. Guibas,et al.  Estimating surface normals in noisy point cloud data , 2004, Int. J. Comput. Geom. Appl..

[11]  Abderrahim Elmoataz,et al.  Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing , 2008, IEEE Transactions on Image Processing.

[12]  Zoltan-Csaba Marton,et al.  Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation , 2012, IEEE Robotics & Automation Magazine.

[13]  Tengyao Wang,et al.  A useful variant of the Davis--Kahan theorem for statisticians , 2014, 1405.0680.

[14]  François Goulette,et al.  POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY , 2010 .

[15]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[16]  Gene Cheung,et al.  Local 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation , 2018, 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP).

[17]  Pierre Vandergheynst,et al.  Graph-based denoising for time-varying point clouds , 2015, 2015 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON).

[18]  Abderrahim Elmoataz,et al.  Nonlocal PDEs-Based Morphology on Weighted Graphs for Image and Data Processing , 2011, IEEE Transactions on Image Processing.