Efficient triangulation based on 3D Euclidean optimization

This paper presents a method for triangulation of 3D points given their projections in two images. Recent results show that the triangulation mapping can be represented as a linear operator K applied to the outer product of corresponding homogeneous image coordinates, leading to a triangulation of very low computational complexity. K can be determined from the camera matrices, together with a so-called blind plane, but we show here that it can be further refined by a process similar to Gold Standard methods for camera matrix estimation. In particular, it is demonstrated that K can be adjusted to minimize the Euclidean L1 residual 3D error, bringing it down to the same level as the optimal triangulation by Hartley and Sturm. The resulting K optimally fits a set of 2D+2D+3D data where the error is measured in the 3D space. Assuming that this calibration set is representative for a particular application, where later only the 2D points are known, this K can be used for triangulation of 3...

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Klas Nordberg
• Keywords: Computer Vision, ICPR 2008, Optimal Triangulation, Residual 3D Error, Triangulation Mapping