3D shape recovery of polyp using two light sources endoscope

As a method to recover 3D shape under point light source and perspective projection, a method to recover the depth distribution has been proposed using optimization with both photometric and geometrical constraints which represents the relation between an interesting point and neighboring points under the assumption of Lambertian reflectance. This method assumes one light source at the same positions of viewing point and point light source although actual endoscope has two light sources. This paper proposes a new approach using a photometric constraint equation considering two light sources. The procedures are as follows. First, obtain depth distributions by optimizing photometric constraint under two light sources. Next, obtain the surface normal vector from depth using numerical difference at each point. Then the mapping between the obtained normal vector and true normal vector is learned by Radial Basis Function Neural Network (NN) for a Lambertian sphere and generalized to another target image. Finally, optimize the depth using photometric constraint to recover the final 3D shape. The validity of this method is confirmed in comparison with the previous methods via computer simulation and experiments using actual endoscope images.