Computer vision researchers have been frustrated in their attempts to automatically derive depth information from conventional two-dimensional intensity images. Research on "shape from texture", "shape from shading", and "shape from focus" is still in a laboratory stage and had not seen much use in commercial machine vision systems. A range image or a depth map contains explicit information about the distance from the sensor to the object surfaces within the field of view in the scene. Information about "surface geometry" which is important for, say, three-dimensional object recognition is more easily extracted from "2 1/2 D" range images than from "2D" intensity images. As a result, both active sensors such as laser range finders and passive techniques such as multi-camera stereo vision are being increasingly utilized by vision researchers to solve a variety of problems. This book contains chapters written by distinguished computer vision researchers covering the following areas: Overview of 3D Vision Range Sensing Geometric Processing Object Recognition Navigation Inspection Multisensor Fusion A workshop report, written by the editors, also appears in the book. It summarizes the state of the art and proposes future research directions in range image sensing, processing, interpretation, and applications. The book also contains an extensive, up-to-date bibliography on the above topics. This book provides a unique perspective on the problem of three-dimensional sensing and processing; it is the only comprehensive collection of papers devoted to range images. Both academic researchers interested in research issues in 3D vision and industrial engineers in search of solutions to particular problems will find this a useful reference book.

1 Report: 1988 NSF Range Image Understanding Workshop.- 1.1 Introduction.- 1.2 Issues in Sensing and Sensors.- 1.3 Early Processing.- 1.4 Obejct Recognition.- 1.5 Sensor Integration.- 1.6 Range Sensing for Navigation.- 1.7 Applications Group Report.- 1.8 Appendix.- 2 A Rule-Based Approach to Binocular Stereopsis.- 2.1 Introduction..- 2.2 The MPG Approach to Binocular Fusion.- 2.3 Review of Procedures for Stereo Matching Under High-level Constraints.- 2.4 Matching Methods Included in the Rule-based Program.- 2.5 A Review of Some Important Rules.- 2.6 Experimental Results.- 2.7 Conclusions.- 3 Geometric Signal Processing.- 3.1 Introduction.- 3.2 Machine Perception.- 3.3 Geometric Representations.- 3.4 Geometric Sensors.- 3.5 Geometric Signal Modeling.- 3.6 Geometric Descriptions.- 3.7 Geometric Approximation.- 3.8 Robust Approximation.- 3.9 Emerging Themes.- 4 Segmentation versus object representation - are they separable?.- 4.1 Introduction.- 4.2 The Role of Shape Primitives.- 4.3 Segmentation Process.- 4.4 Control Structure.- 4.5 Results.- 4.6 Summary.- 5 Object Recognition.- 5.1 Introduction.- 5.2 Aspects of the Object Recognition Problem.- 5.3 Recognition via Matching Sensed Data to Models.- 5.4 The Statistical Pattern Recognition Approach.- 5.5 Object Represented as Geometric Aggregate.- 5.6 Object as an Articulated Set of Parts.- 5.7 Concluding Discussion.- 6 Applications of Range Image Sensing and Processing.- 6.1 Introduction.- 6.2 Major Industrial Application Areas.- 6.3 Obstacles to Practical Application.- 6.4 Conclusion.- 7 3-D Vision Techniques for Autonomous Vehicles.- 7.1 Introduction.- 7.2 Active range and reflectance sensing.- 7.3 Terrain representations.- 7.4 Combining multiple terrain maps.- 7.5 Combining range and intensity data.- 7.6 Conclusion.- 8Multisensor Fusion for Automatic Scene Interpretation.- 8.1 Introduction.- 8.2 Image Models.- 8.3 Intersensory Verification of Image Features.- 8.4 Intersensory Verification from Physical Principles.- 8.5 Multisensory Vision - An Illustrative Example.- 8.6 Conclusions.