3D Part Segmentation Using Simulated Electrical Charge Distributions

A novel approach to 3D part segmentation is presented. It is a well-known physical fact that electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and vanish at a sharp concavity. Thus, object part boundaries, which are usually denoted by a sharp surface concavity, can be detected by simulating the electrical charge density over the object surface and locating surface points which exhibit local charge density minima. Beginning with single- or multiview range data of a 3D object, we simulate the charge density distribution over an object's surface which has been tessellated by a triangular mesh. We detect the deep surface concavities by tracing local charge density minima and then decompose the object into parts at these points. The charge density computation does not require an assumption on surface smoothness and uses weighted global data to produce robust local surface features for part segmentation.

[1]  Ruzena Bajcsy,et al.  Volumetric segmentation of range images of 3D objects using superquadric models , 1993 .

[2]  Donald D. Hoffman,et al.  Parts of recognition , 1984, Cognition.

[3]  Michael Brady,et al.  The Curvature Primal Sketch , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Olivier D. Faugeras,et al.  Polyhedral approximation of 3-D objects without holes , 1984, Comput. Vis. Graph. Image Process..

[5]  Larry S. Davis,et al.  Labeling of human face components from range data , 1994 .

[6]  R. Price,et al.  The lightning‐rod fallacy , 1985 .

[7]  Gérard G. Medioni,et al.  Surface description of complex objects from multiple range images , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Frank P. Ferrie,et al.  Partitioning range images using curvature and scale , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Gérard G. Medioni,et al.  Part decomposition and description of 3D shapes , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[10]  Richard Barrett,et al.  Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods , 1994, Other Titles in Applied Mathematics.

[11]  Irving Biederman,et al.  Human image understanding: Recent research and a theory , 1985, Comput. Vis. Graph. Image Process..

[12]  A. Pentland Recognition by Parts , 1987 .

[13]  Robert B. Fisher,et al.  Experiments in Curvature-Based Segmentation of Range Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Satoshi Suzuki,et al.  3D parts decomposition from sparse range data using information criterion , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ruzena Bajcsy,et al.  Generalised cylinders from local aggregation of sections , 1981, Pattern Recognit..

[16]  Ramesh C. Jain,et al.  Invariant surface characteristics for 3D object recognition in range images , 1985, Comput. Vis. Graph. Image Process..

[17]  Frank P. Ferrie,et al.  Deriving course 3D models of objects , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Herbert Freeman,et al.  Shape description via the use of critical points , 1978, Pattern Recognit..

[19]  D. Wilton,et al.  Potential integrals for uniform and linear source distributions on polygonal and polyhedral domains , 1984 .

[20]  Anil K. Jain,et al.  Segmentation and Classification of Range Images , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  K Siddiqi,et al.  Parts of Visual Form: Psychophysical Aspects , 1996, Perception.

[22]  Emanuele Trucco Part segmentation of slice data using regularity , 1993, Signal Process..

[23]  A.K. Jain,et al.  Obtaining generic parts from range images using a multi-view represen-tation , 1994 .

[24]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  B. O'neill Elementary Differential Geometry , 1966 .

[26]  Frank P. Ferrie,et al.  Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  J. Koenderink,et al.  The Shape of Smooth Objects and the Way Contours End , 1982, Perception.

[28]  Ruzena Bajcsy,et al.  Packing Volumes by Spheres , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Azriel Rosenfeld,et al.  Decomposition and approximation of three-dimensional solids , 1986, Comput. Vis. Graph. Image Process..

[30]  Harry Shum,et al.  An integral approach to free-formed object modeling , 1995, Proceedings of IEEE International Conference on Computer Vision.

[31]  Martin D. Levine,et al.  Computing parametric geon descriptions of 3d multi-part objects , 1996 .

[32]  Azriel Rosenfeld,et al.  3-D Shape Recovery Using Distributed Aspect Matching , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Martin D. Levine,et al.  3-D shape approximation using parametric geons , 1997, Image Vis. Comput..

[34]  M. Leyton Inferring Causal History from Shape , 1989 .

[35]  Denis Laurendeau,et al.  A General Surface Approach to the Integration of a Set of Range Views , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Ruzena Bajcsy,et al.  Segmentation versus object representation—are they separable? , 1989 .

[37]  D. A. Dunnett Classical Electrodynamics , 2020, Nature.

[38]  G. Temple Static and Dynamic Electricity , 1940, Nature.

[39]  Norman I. Badler,et al.  Decomposition of Three-Dimensional Objects into Spheres , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Dimitris N. Metaxas,et al.  Adaptive shape evolution using blending , 1995, Proceedings of IEEE International Conference on Computer Vision.

[41]  Alex Pentland,et al.  Part Segmentation for Object Recognition , 1989, Neural Computation.

[42]  F. Solina,et al.  A direct part-level segmentation of range images using volumetric models , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[43]  Martin D. Levine,et al.  Representing 3-D Objects in Range Images Using Geons , 1996, Comput. Vis. Image Underst..