Determining Surface Orientation by Projecting a Stripe Pattern
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Orientation from a Projected Grid
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Surface Solution Using Structured Light and Constraint Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterizing Three-Dimensional Surface Structures from Visual Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Reconstruction Using Deformable Models with Interior and Boundary Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Vision Driven Automatic Assembly Unit
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Fast acquisition of dense depth data by a new structured light scheme
Computer Vision and Image Understanding
3D Structure Recovery and Unwarping of Surfaces Applicable to Planes
International Journal of Computer Vision
Fast acquisition of dense depth data by a new structured light scheme
Computer Vision and Image Understanding
Highlighted depth-of-field photography: Shining light on focus
ACM Transactions on Graphics (TOG)
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In this correspondence, algorithms are introduced to infer surface orientation and structure of visible object surfaces using grid coding. We adopt the active lighting technique to spatially ``encode'' the scene for analysis. The observed objects, which can have surfaces of arbitrary shape, are assumed to rest on a plane (base plane) in a scene which is ``encoded'' with light cast through a grid plane. Two orthogonal grid patterns are used, where each pattern is obtained with a set of equally spaced stripes marked on a glass pane. The scene is observed through a camera and the object surface orientation is determined using the projected patterns on the object surface. If the surfaces under consideration obey certain smoothness constraints, a dense orientation map can be obtained through proper interpolation. The surface structure can then be recovered given this dense orientation map. Both planar and curved surfaces can be handled in a uniform manner. The algorithms we propose yield reasonably accurate results and are relatively tolerant to noise, especially when compared to shape-from-shading techniques. In contrast to other grid coding techniques reported which match the grid junctions for depth reconstruction under the stereopsis principle, our techniques use the direction of the projected stripes to infer local surface orientation and do not require any correspondence relationship between either the grid lines or the grid junctions to be specified. The algorithm has the ability to register images and can therefore be embedded in a system which integrates knowledge from multiple views.