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Range image understanding
EDITORIAL Range Image Understanding Guest Editors: Dr J K Aggatwal and Dr B C Vemuri
The understanding of range images is a difficult yet an important area of research in computer vision. Advances in sensor technology for direct 3D sensing in conjunction with progess in passive range sensing methods has resulted in a tremendous growth of research efforts in 3D computer vision. Moreover, the burden of processing range data has been alleviated with the advent of faster hardware in the form of a variety of multi-processing systems, and this has further provided impetus for research in this field of computer vision. However, problems of 3D representation, and matching in static as well as dynamic real world scenes, are formidable research tasks currently being addressed by many researchers. The goal of this special issue is to gather significant research results on 3D object representation, matching for pose determination and object recognition in static and dynamic environments into one volume. This issue is organized into two parts: the first part contains three regular papers addressing issues of 3D object representation, model construction and object recognition. The second part consists of four correspondences which discuss various issues ranging from range data acquisition, shape representation for discrimination purposes, to the analysis of dynamic range data for the purposes of object shape representation and recognition. The papers describe novel contributions in various aspects of range image understanding. The paper by Delingette et al. presents a representation technique that makes clever use of the deformable surfaces to segment 3D objects in a cluttered unstructured environment. Chen and Medioni address the important and difficult task of building 3D models of complex shapes via registration of multiple range images acquired from different viewpoints of an object. Fisher argues how most 3D object recognition systems do not make use of salient visual features, and presents
~0110 no 3 aprill992
several second-order volumetric primrtives that can be used in representation as well as recognition of objects. The second part of the issue begins with the paper by Hebert and Krotkov. who discuss the important problem of accuracy in 3D data acquisition techniques, followed by Raja and Jain. who descrrbe an interesting technique for shape discrimination from range data using superquadrics surfaces. Tirumalai et al. present a recursive computational method that yields a boundary-level shape description from dynamic stereo data with possible applications in obstacle avoidance and path planning in mobile robots. Reid and Brady present the progress in equipping the Oxford Autonomous Guided Vehicle with range sensing and recognition capabilities. They describe a system which covers several aspects of range image understanding, including sensing, preprocessing of the sensed images, building a representation, and finally, object recognition. Finally, a few words on the logistics of the special issue. Twenty papers were received for possible publication in the special issue. Each was independently reviewed by two vision researchers, and the selection was solely based on the reviewers comments which were transmitted verbatim to the authors. We are grateful to the many vision researchers who agreed to serve in the capacity of reviewers, and were able to provide the reviews at a short notice. A word of special thanks to the Group Editor of Butterworth-Heinemann Ltd, MS Karen Panaghiston, without whose coordination skills this issue would have been further delayed.
J K Aggarwal University of Texas at .4ustin, TX, USA B C Vemuri University of Florida at Gainesville, FL, USA
131
The understanding of range images is a difficult yet an important area of research in computer vision. Advances in sensor technology for direct 3D sensing in conjunction with progess in passive range sensing methods has resulted in a tremendous growth of research efforts in 3D computer vision. Moreover, the burden of processing range data has been alleviated with the advent of faster hardware in the form of a variety of multi-processing systems, and this has further provided impetus for research in this field of computer vision. However, problems of 3D representation, and matching in static as well as dynamic real world scenes, are formidable research tasks currently being addressed by many researchers. The goal of this special issue is to gather significant research results on 3D object representation, matching for pose determination and object recognition in static and dynamic environments into one volume. This issue is organized into two parts: the first part contains three regular papers addressing issues of 3D object representation, model construction and object recognition. The second part consists of four correspondences which discuss various issues ranging from range data acquisition, shape representation for discrimination purposes, to the analysis of dynamic range data for the purposes of object shape representation and recognition. The papers describe novel contributions in various aspects of range image understanding. The paper by Delingette et al. presents a representation technique that makes clever use of the deformable surfaces to segment 3D objects in a cluttered unstructured environment. Chen and Medioni address the important and difficult task of building 3D models of complex shapes via registration of multiple range images acquired from different viewpoints of an object. Fisher argues how most 3D object recognition systems do not make use of salient visual features, and presents
~0110 no 3 aprill992
several second-order volumetric primrtives that can be used in representation as well as recognition of objects. The second part of the issue begins with the paper by Hebert and Krotkov. who discuss the important problem of accuracy in 3D data acquisition techniques, followed by Raja and Jain. who descrrbe an interesting technique for shape discrimination from range data using superquadrics surfaces. Tirumalai et al. present a recursive computational method that yields a boundary-level shape description from dynamic stereo data with possible applications in obstacle avoidance and path planning in mobile robots. Reid and Brady present the progress in equipping the Oxford Autonomous Guided Vehicle with range sensing and recognition capabilities. They describe a system which covers several aspects of range image understanding, including sensing, preprocessing of the sensed images, building a representation, and finally, object recognition. Finally, a few words on the logistics of the special issue. Twenty papers were received for possible publication in the special issue. Each was independently reviewed by two vision researchers, and the selection was solely based on the reviewers comments which were transmitted verbatim to the authors. We are grateful to the many vision researchers who agreed to serve in the capacity of reviewers, and were able to provide the reviews at a short notice. A word of special thanks to the Group Editor of Butterworth-Heinemann Ltd, MS Karen Panaghiston, without whose coordination skills this issue would have been further delayed.
J K Aggarwal University of Texas at .4ustin, TX, USA B C Vemuri University of Florida at Gainesville, FL, USA
131