Robot vision
Computation of Surface Orientation and Structure of Objects Using Grid Coding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining Surface Orientation by Projecting a Stripe Pattern
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
A Vision Driven Automatic Assembly Unit
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Towards a real-time 3D shape reconstruction using a structured light system
Pattern Recognition
3D shape recovery by the use of single image plus simple pattern illumination
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Nonstructured light-based sensing for 3D reconstruction
Pattern Recognition
Highlighted depth-of-field photography: Shining light on focus
ACM Transactions on Graphics (TOG)
3-D face modeling from two views and grid light
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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Two simple methods are given for obtaining the surface shape using a projected grid. After the camera is calibrated to the 3-D workspace, the only input date needed for the computation of surface normals are grid intersect points in a single 2-D image. The first method performs nonlinear computations based on the distortion of the lengths of the grid edges and does not require a full calibration matrix. The second method requires that a full parallel projection model of the imaging is available, which enables it to compute 3-D normals using simple linear computations. The linear method performed better overall in the experiments, but both methods produced normals within 4-8 degrees of known 3-D directions. These methods appear to be superior to methods based on shape-from-shading because the results are comparable, yet the equipment setup is simpler and the processing is not very sensitive to object reflectance.