Geometric Priors for Gaussian Process Implicit Surfaces

This paper presents an extension of Gaussian process implicit surfaces (GPIS) by the introduction of geometric object priors. The proposed method enhances the probabilistic reconstruction of objects from three-dimensional (3-D) pointcloud data, providing a rigorous way of incorporating prior knowledge about objects expected in a scene. The key ideas, including the systematic use of surface normal information, are illustrated with one-dimensional and two-dimensional examples, and then applied to simulated and real pointcloud data for 3-D objects. The performance of our method is demonstrated in two different application scenarios, using complete and partial surface observations. Qualitative and quantitative analysis of the results reveals the superiority of the proposed approach over existing GPIS configurations that do not exploit prior knowledge.

[1]  Carl E. Rasmussen,et al.  Derivative Observations in Gaussian Process Models of Dynamic Systems , 2002, NIPS.

[2]  R. Adler.  The Geometry of Random Fields , 2009 .

[3]  Alois Knoll,et al.  Object Recognition Using Constraints from Primitive Shape Matching , 2014, ISVC.

[4]  Marcos P. Gerardo-Castro,et al.  Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces , 2013, FSR.

[5]  Andreas Uhl,et al.  BlenSor: Blender Sensor Simulation Toolbox , 2011, ISVC.

[6]  Sanjiv Singh,et al.  Automated Visual Yield Estimation in Vineyards , 2014, J. Field Robotics.

[7]  Salah Sukkarieh,et al.  Orchard fruit segmentation using multi-spectral feature learning , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Danica Kragic,et al.  Enhancing visual perception of shape through tactile glances , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Tat-Jun Chin,et al.  Interacting Geometric Priors For Robust Multimodel Fitting , 2014, IEEE Transactions on Image Processing.

[10]  Matei T. Ciocarlie,et al.  GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Andrew Fitzgibbon,et al.  Gaussian Process Implicit Surfaces , 2006 .

[12]  Oscar Dalmau Cedeño,et al.  GP-MPU Method for Implicit Surface Reconstruction , 2014, MICAI.

[13]  Jonghyuk Kim,et al.  Hierarchical Gaussian Processes for Robust and Accurate Map Building , 2015, ICRA 2015.

[14]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[15]  Marc Toussaint,et al.  Uncertainty aware grasping and tactile exploration , 2013, 2013 IEEE International Conference on Robotics and Automation.

[16]  Fabio Tozeto Ramos,et al.  Gaussian process occupancy maps* , 2012, Int. J. Robotics Res..

[17]  Florentin Wörgötter,et al.  Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Anthony J. Yezzi,et al.  Robust 3D Pose Estimation and Efficient 2D Region-Based Segmentation from a 3D Shape Prior , 2008, ECCV.

[19]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Stefano Soatto,et al.  A Pseudo-distance for Shape Priors in Level Set Segmentation , 2003 .

[21]  Geoffrey A. Hollinger,et al.  Active planning for underwater inspection and the benefit of adaptivity , 2012, Int. J. Robotics Res..

[22]  Marc Toussaint,et al.  Gaussian process implicit surfaces for shape estimation and grasping , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[24]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[25]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.