Abstract
Laser scanning range sensors are widely used for high-precision, high-density three-dimensional (3D) reconstruction and inspection of the surface of physical objects. The process typically involves planning a set of views, physically altering the relative object-sensor pose, taking scans, registering the acquired geometric data in a common coordinate frame of reference, and finally integrating range images into a nonredundant model. Efficiencies could be achieved by automating or semiautomating this process. While challenges remain, there are adequate solutions to semiautomate the scan-register-integrate tasks. On the other hand, view planning remains an open problem---that is, the task of finding a suitably small set of sensor poses and configurations for specified reconstruction or inspection goals. This paper surveys and compares view planning techniques for automated 3D object reconstruction and inspection by means of active, triangulation-based range sensors.
- Abrams, S. 1997. Sensor planning in an active robot work-cell. Ph.D. dissertation, Columbia University, New York, NY. Google Scholar
- Amann, M.-C., Bosch, T., Myllyla, R., and Rioux, M. 2001. Laser ranging: a critical review of usual techniques for distance measurement. Opt. Eng. 40, 1, 10--19.Google Scholar
- Arbel, T. and Ferrie, F. 1999. Entropy-based gaze planning. In Proceedings of the 2nd IEEE Workshop on Perception for Mobile Agents, in association with 1999 IEEE Computer Society Conferrence on Computer Vision and Pattern Recognition (Fort Collins, CO.). 87--94.Google Scholar
- Aubry, S. 1994. 3D model construction from multiple sensor viewpoints. Ph.D. dissertation, McGill University, Montreal, P.Q., Canada.Google Scholar
- Banta, J. and Abidi, M. 1996. Autonomous placement of a range sensor for acquisition of optimal 3D models. In Proceedings of the IEEE 22nd International Conference on Industrial Electronics, Control and Instrumentation (Taipei, Taiwan). 1583--1588.Google Scholar
- Banta, J., Zhien, Y., Wang, X., Zhang, G., Smith, M., and Abidi, M. 1995. A best-next-view algorithm for three-dimensional scene reconstruction using range images. SPIE 2588, 418--429.Google Scholar
- Banta, J. E. 1996. Optimal range sensor positioning for three-dimensional model reconstruction. M.S. thesis, University of Tennessee, Knoxville, TN.Google Scholar
- Baribeau, R., Rioux, M., and Godin, G. 1991. Color reflectance modeling using a polychromatic laser range sensor. IEEE Trans. PAMI 14, 2 (Feb.), 263--269. Google Scholar
- Batchelor, B. 1989. A prolog lighting advisor. In Proceedings of the SPIE Conference on Intelligent Robots and Computer Vision VIII: Systems and Applications. Vol. 1193. 295--302.Google Scholar
- Beraldin, J.-A., El-Hakim, S., and Cournoyer, L. 1993. Practical range camera calibration. In Proceedings of the SPIE Conference on Videometrics II (Boston, MA). Vol. 2067. 21--30.Google Scholar
- Besl, P. 1989. Range image sensors. In Advances in Machine Vision, J. Sanz, Ed. Springer-Verlag, New York, NY.Google Scholar
- Besl, P. and McKay, N. 1992. A method for registration of 3D shapes. IEEE Trans. PAMI 14, 2 (Feb.), 239--256. Google Scholar
- Best, L. C. 1996. Autonomous construction of three-dimensional models from range data. Ph.D. dissertation, University of Wyoming, Laramie, WY. Google Scholar
- Bowyer, K. and Dyer, C. 1990. Aspect graphs: An introduction and survey of recent results. In Proceedings of the SPIE Conference on Close-Range Photogrammetry Meets Machine Vision. Vol. 1395. 200--208.Google Scholar
- Buzinski, M. J. 1990. A comparison of touch probe and laser triangulation sensors for verifying the dimensional accuracy of manufactured parts. M.S. thesis, Purdue University, Lafayette, IN.Google Scholar
- Connolly, C. 1985. The determination of next best views. In Proceedings of the IEEE International Conference on Robotics and Automation. 432--435.Google Scholar
- Cowan, C. and Kovesi, P. 1988. Automatic sensor placement from vision task requirements. IEEE Trans. PAMI 10, 3 (May), 407--416. Google Scholar
- Curless, B. and Levoy, M. 1996. Better optical triangulation through space-time analysis. In SIGGRAPH '96. 1--10.Google Scholar
- Curless, B. L. 1997. New methods for surface reconstruction from range images. Ph.D. dissertation, Stanford University, Stanford, CA. Google Scholar
- El-Hakim, S. and Beraldin, J.-A. 1994. On the integration of range and intensity data to improve vision-based three-dimensional measurements. In Proceedings of the SPIE Conference on Videometrics III (Boston, MA). Vol. 2350. 306--321.Google Scholar
- Faugeras, O., Mundy, J., Ahuja, N., Dyer, C., Pentland, A., Jain, R., and Ikeuchi, K. 1992. Why aspect graphs are not (yet) practical for computer vision. CVGIP: Image Understand. 55, 2, 212--218. Google Scholar
- Garcia, M., Velazquez, S., and Sappa, A. 1998a. A two-stage algorithm for planning the next view from range images. In British Machine Vision Conference 1998. 720--729.Google Scholar
- Garcia, M., Velazquez, S., Sappa, A., and Basanez, L. 1998b. Autonomous sensor planning for 3D reconstruction of complex objects from range images. In Proceedings of the IEEE International Conference on Robotics and Automation (Leuven, Belgium). 3085--3090.Google Scholar
- Greespan, M. 2002. Geometric probing of dense range data. IEEE Trans. PAMI 24, 4 (April), 495--508. Google Scholar
- Hall-Holt, O. 1998. Technical report, Stanford University, Stanford, CA. Available online at www-graphics.stanford.edu/∼olaf/nbv.html.Google Scholar
- Hébert, P. 2001. A self-referenced hand-held range sensor. In 3rd International Conference on 3D Digital Imaging and Modeling (Quebec City, P.Q., Canada). 5--12.Google Scholar
- Huber, D. 2001. Automatic 3D modeling using range images obtained from unknown viewpoints. In 3rd International Conference on 3D Digital Imaging and Modeling (Quebec City, P.Q., Canada). 153--160.Google Scholar
- Hutchinson, S. and Kak, A. 1989. Planning sensing strategies in a robot work cell with multi-sensor capabilities. IEEE Trans. Robot. Automat. 5, 6 (Dec.), 765--783.Google Scholar
- Kahn, J., Klawe, M., and Kleitman, D. 1980. Traditional galleries require fewer watchmen. Tech. rep. RJ3021, IBM Watson Research Center, Yorktown Heights, NY.Google Scholar
- Kitamura, Y., Sato, H., and Tamura, H. 1990. An expert system for industrial machine vision. In Proceedings of the 10th International Conference on Pattern Recognition. 771--773.Google Scholar
- Kutulakos, K. and Seitz, S. 2000. A theory of shape by space carving. Intl. J. Comp. Vis. 38, 3, 1999--218. Google Scholar
- Kutulakos, K. N. 1994. Exploring three-dimensional objects by controlling the point of observation. Ph.D. dissertation, University of Wisconsin-Madison, Madison, WI. Google Scholar
- Lamb, D., Baird, D., and Greenspan, M. 1999. An automation system for industrial 3D laser digitizing. In Proceedings of the 2nd International Conference on 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 148--157. Google Scholar
- Langis, C., Greenspan, M., and Godin, G. 2001. The parallel iterative closest point algorithm. In Proceedings of the 3rd International Conference on 3D Digital Imaging and Modeling (Quebec City, P.Q., Canada). 195--202.Google Scholar
- Massios, N. 1997. Predicting the best next view for a laser range striper system. M.S. thesis, University of Edinburgh, Edinburgh, Scottland.Google Scholar
- Massios, N. and Fisher, R. 1998. A best next view selection algorithm incorporating a quality criterion. In British Machine Vision Conference (Sept. 1998). 780--789.Google Scholar
- Maver, J. 1995. Collecting visual information using an active sensor system. Ph.D. dissertation, University of Ljubljana, Ljubljana, Slovenia.Google Scholar
- Maver, J. and Bajcsy, R. 1990. How to decide form the first view where to look next. In Proceedings of the DARPA Image Understanding Workshop (Pittsburgh, PA). 482--496.Google Scholar
- Maver, J. and Bajcsy, R. 1993. Occlusions as a guide for planning the next view. IEEE Trans. PAMI 17, 5 (May), 417--433. Google Scholar
- Milroy, M., Bradley, C., and Vickers, G. 1996. Automated laser scanning based on orthogonal cross sections. Machine Vis. Appl. 9, 106--118. Google Scholar
- Morooka, K., Zha, H., and Hasegawa, T. 1999. Computations on a spherical view space for efficient planning of viewpoints in 3D object modeling. In Proceedings of the 2nd International Conference on 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 138--147. Google Scholar
- Newman, T. and Jain, A. 1995. A survey of automated visual inspection. Comput. Vis. Image Understand. 61, 2 (March), 231--262. Google Scholar
- Novini, A. 1988. Lighting and optics expert system for machine vision. In Proceedings of the SPIE Conference on Optics, Illumination, and Image Sensing for Machine Vision III. Vol. 1005. 131--136.Google Scholar
- Papadopoulos-Orfanos, D. 1997. Numerisation geometrique automatique a l'aide d'une camera 3D de precision a profondeur de champ reduite. Ph.D. dissertation, Ecole nationale superieure des telecommunications, Paris, France.Google Scholar
- Papadopoulos-Orfanos, D. and Schmitt, F. 1997. Automatic 3D digitization using a laser rangefinder with a small field of view. In Proceedings of the International Conference on Recent Advances in 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 60--67. Google Scholar
- Pito, R. 1996a. Mesh integration based on co-measurements. In Proceedings of the International Conference on Image Processing (Lausanne, Switzerland). Vol. 2. 397--400.Google Scholar
- Pito, R. 1996b. A sensor based solution to the next best view problem. In Proceedings of the International Conference on Pattern Recognition. 941--945. Google Scholar
- Pito, R. 1997a. Automated surface acquisition using range cameras. Ph.D. dissertation, University of Pennsylvania, Philadelphia, PA. Google Scholar
- Pito, R. 1997b. A registration aid. In Proceedings of the International Conference on Recent Advances in 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 85--92. Google Scholar
- Pito, R. 1999. A solution to the next best view problem for automated surface acquisition. IEEE Trans. PAMI 21, 10 (Oct.), 1016--1030. Google Scholar
- Pito, R. and Bajcsy, R. 1995. A solution to the next-best-view problem for automated cad model acquisition of free-form objects using range cameras. In Proceedings of the SPIE Symposium on Modeling, Simulation and Control Technologies for Manufacturing (Philadelphia, PA.) Vol. 2596. 78--89.Google Scholar
- Prieto, F. 1999. Métrologie assistée par ordinateur: Apport des capteurs 3D sans contact. Ph.D. dissertation, L'Institut National des Sciences Appliquées de Lyon, Lyon, France.Google Scholar
- Prieto, F., Redarce, T., Boulanger, P., and Lepage, R. 1999. CAD-based range sensor placement for optimum 3D data acquisition. In Proceedings of the International Conference on 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 128--137. Google Scholar
- Prieto, F., Redarce, T., Boulanger, P., and Lepage, R. 2001. Tolerance control with high resolution 3D measurements. In 3rd International Conference on 3D Digital Imaging and Modeling (Quebec City, P.Q., Canada). 339--346.Google Scholar
- Pudney, C. J. 1994. Surface modeling and surface following for robots equipped with range sensors. Ph.D. dissertation, University of Western Australia, Perth, Australia.Google Scholar
- Reed, M. and Allen, P. 1997. A robotic system for 3D model acquisition from multiple range images. In Proceedings of the IEEE International Conference on Robotics and Automation (Albuquerque, NM.). 2509--2514.Google Scholar
- Reed, M. and Allen, P. 1999. 3D modeling from range imagery. Image Vis. Comput. 17, 1 (Feb.), 99--111.Google Scholar
- Reed, M., Allen, P., and Stamos, I. 1997a. 3D modeling from range imagery: An incremental method with a planning component. In Proceedings of the International Conference on Recent Advances in 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 76--83. Google Scholar
- Reed, M., Allen, P., and Stamos, I. 1997b. Automated model acquisition from range images with view planning. In Proceedings of the IEEE Conference Visual Pattern Recognition (San Juan, Puerto Rico). 72--77. Google Scholar
- Reed, M. K. 1998. Solid model acquisition form range imagery. Ph.D. dissertation, Columbia University, New York, NY. Google Scholar
- Roberts, D. and Marshall, A. 1997. A review of viewpoint planning. Tech. Rep. 97007, University of Wales College of Cardiff, Cardiff, U.K.Google Scholar
- Roth, G. 2000. Building models from sensor data: an application shared by the computer vision and the computer graphics community. In Confluence of Computer Vision and Computer Graphics, A. Leonardis et al., Eds. Kluwer The Hague, The Netherlands. Google Scholar
- Roui-Abidi, B. 1992. Automatic sensor placement for volumetric object characterization. Ph.D. dissertation, University of Tennessee, Knoxville, TN. Google Scholar
- Rusinkiewicz, S. and Levoy, M. 2001. Efficient variants of the ICP algorithm. In Proceedings of the 3rd International Conference on 3D Digital Imaging and Modeling (Quebec City, P.Q., Canada). 145--152.Google Scholar
- Sakane, S., Niepold, R., Sato, T., and Shirai, Y. 1992. Illumination setup planning for a hand-eye system based on an environmental model. Advan. Robot. 6, 4, 461--482.Google Scholar
- Scott, W., Roth, G., and Rivest, J.-F. 2000. Performance-oriented view planning for automatic model acquisition. In Proceedings of the 31st International Symposium on Robotics (Montreal, P.Q., Canada). 314--319.Google Scholar
- Scott, W., Roth, G., and Rivest, J.-F. 2001a. View planning as a set covering problem. Tech. Rep. NRC-44892. National Research Council of Canada, Institute for Information Technology, Ottawa, Ont., Canada.Google Scholar
- Scott, W., Roth, G., and Rivest, J.-F. 2001b. View planning for multi-stage object reconstruction. In Proceedings of the 14th International Conference on Vision Interface (Ottawa, Ont., Canada). 64--71.Google Scholar
- Scott, W., Roth, G., and Rivest, J.-F. 2002. Pose error effects on range sensing. In Proceedings of the 15th International Conference on Vision Interface (Calgary, Alta., Canada). 331--338.Google Scholar
- Scott, W. R. 2002. Performance-oriented view planning for automated object reconstruction. Ph.D. dissertation, University of Ottawa, Ottawa, Ont., Canada. Google Scholar
- Sedas-Gersey, S. W. 1993. Algorithms for automatic sensor placement to acquire complete and accurate information. Ph.D. dissertation, Carnegie Mellon University, Pittsburgh, PA. Google Scholar
- Sequeira, V. 1996. Active range sensing for three-dimensional environment reconstruction. Ph.D. dissertation, IST-Technical University of Lisbon, Lisbon, Portugal.Google Scholar
- Sequeira, V. and Gonçalves, J. 2002. 3D reality modeling: Photo-realistic 3D models of real world scenes. Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission (3DPVT02). 776--783.Google Scholar
- Soucy, G., Callari, F., and Ferrie, F. 1998. Uniform and complete surface coverage with a robot-mounted laser rangefinder. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (Victoria, B.C., Canada). 1682--1688.Google Scholar
- Tarabanis, K. 1992. Sensor planning and modeling for machine vision tasks. Ph.D. dissertation, Columbia University, New York, NY. Google Scholar
- Tarabanis, K., Allen, P., and Tsai, R. 1995a. A survey of sensor planning in computer vision. IEEE Trans. Robot. Automat. 11, 1 (Feb.), 86--104.Google Scholar
- Tarabanis, K., Tsai, R., and Allen, P. 1995b. The MVP sensor planning system for robotic vision tasks. IEEE Trans. Robot. Automat. 11, 1 (Feb.), 72--85.Google Scholar
- Tarabanis, K., Tsai, R., and Kaul, A. 1996. Computing occlusion-free viewpoints. IEEE Trans. PAMI 18, 3 (March), 279--292. Google Scholar
- Tarbox, G. and Gottschlich, S. 1995. Planning for complete sensor coverage in inspection. Comput. Vis. Image Understand. 61, 1 (Jan.), 84--111. Google Scholar
- Tarbox, G. H. 1993. A volumetric approach to automated 3D inspection. Ph.D. dissertation. Rensselaer Polytechnic Institute, Troy, NY. Google Scholar
- Tremblay, P.-J. and Ferrie, F. 2000. The skeptical explorer: A multi-hypotheses approach to visual modeling and exploration. Auton. Robot. 8, 2, 193--201. Google Scholar
- Trucco, E., Umasuthan, M., Wallace, A., and Roberto, V. 1997. Model-based planning of optimal sensor placements for inspection. IEEE Trans. Robot. Automat. 13, 2 (April), 182--194.Google Scholar
- Urrutia, J. 2000. Art gallery and illumination problems. In Handbook of Computational Geometry, J.-R. Sack and J. Urrutia, Eds. Elsevier, New York, NY.Google Scholar
- Whaite, P. and Ferrie, F. 1990. From uncertainty to visual exploration. In Proceedings of the 3rd International Conference on Computer Vision (Osaka, Japan). 690--697.Google Scholar
- Whaite, P. and Ferrie, F. 1991. From uncertainty to visual exploration. IEEE Trans. PAMI 13, 10 (Oct.), 1038--1049. Google Scholar
- Whaite, P. and Ferrie, F. 1992. Uncertain views. In Proceedings of the IEEE Conference on Visual Pattern Recognition. 3--9.Google Scholar
- Whaite, P. and Ferrie, F. 1997. Autonomous exploration: Driven by uncertainty. IEEE Trans. PAMI 19, 3 (March), 193--205. Google Scholar
- Wheeler, M. D. 1996. Automatic modeling and localization for object recognition. Ph.D. dissertation, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
- Wixson, L. E. 1994. Gaze selection for visual search. Ph.D. thesis, University of Rochester, Rochester, NY. Google Scholar
- Xie, S., Calvert, T., and Bhattacharya, B. 1986. Planning views for the incremental construction of model bodies. In Proceedings of the 8th International Conference on Pattern Recognition (Paris, France). 154--157.Google Scholar
- Ye, Y. 1997. Sensor planning for object search. Ph.D. dissertation, University of Toronto, Toronto, Ont., Canada. Google Scholar
- Ye, Y. and Tsotsos, J. 1999. Sensor planning for 3D object search. Comput. Vis. Image Understand. 73, 2 (Feb.), 145--168. Google Scholar
- Yi, S. 1990. Illumination control expert for machine vision: A goal driven approach. Ph.D. dissertation, University of Washington, Seattle, WA. Google Scholar
- Yi, S., Haralick, R., and Shapiro, L. 1995. Optimal sensor and light source positioning for machine vision. Comput. Vis. Image Understand. 61, 1 (Jan.), 122--137. Google Scholar
- Yu, Y. and Gupta, K. 2000. An information theoretic approach to view point planning for motion planning of eye-in-hand systems. In Proceedings of the 31st International Symposium on Robotics. 306--311.Google Scholar
- Yuan, X. 1993. 3D reconstruction as an automatic modeling system. Ph.D. dissertation, University of Alberta, Edmonton, Alta., Canada. Google Scholar
- Yuan, X. 1995. A mechanism of automatic 3D object modeling. IEEE Trans. PAMI 17, 3 (March), 307--311. Google Scholar
- Zha, H., Morooka, K., and Hasegawa, T. 1998. Next best viewpoint (NBV) planning for active object modeling based on a learning-by-showing approach. In Proceedings of the 3rd Asian Conference on Computer Vision (Hong Kong). 185--192. Google Scholar
- Zha, H., Morooka, K., Hasegawa, T., and Nagata, T. 1997. Active modeling of 3D objects: Planning on the next best pose (NBP) for acquiring range images. In Proceedings of the International Conference on Recent Advances in 3D Digital Imaging and Modeling (Ottawa, Ont., Canada). 68--75. Google Scholar
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