We propose a new method to recover scene points from a single calibrated view using a subset of distances among the points. This paper first introduces the problem and its relationship with the perspective n point problem. Then the number of distances required to uniquely recover scene points are explored. The result is then developed into a practical vision algorithm to calculate the initial points' coordinates using distance constraints. Finally SQP (Sequential Quadratic Programming) is used to optimize the initial estimations. It can minimize a cost function defined as the sum of squared reprojection errors while keeping the specified distance constraints strictly satisfied. Both simulation data and real scene images have been used to test the proposed method, and good results have been obtained.

We propose using SQP (Sequential Quadratic Programming) to directly recover 3D quadratic surface parameters from multiple views. A surface equation is used as a constraint. In addition to the sum of squared reprojection errors defined in the traditional bundle adjustment, a Lagrangian term is added to force recovered points to satisfy the constraint. The minimization is realized by SQP. Our algorithm has three advantages. First, given corresponding features in multiple views, the SQP implementation can directly recover the quadratic surface parameters optimally instead of a collection of isolated 3D points coordinates. Second, the specified constraints are strictly satisfied and the camera parameters and 3D coordinates of points can be determined more accurately than that by unconstrained methods. Third, the recovered quadratic surface model can be represented by a much smaller number of parameters instead of point clouds and triangular patches. Experiments with both synthetic and real images show the power of this approach.