The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Can two specular pixels calibrate photometric stereo

Lambertian photometric stereo with unknown light source parameters is ambiguous. Provided that the object imaged constitutes a surface, the ambiguity is represented by the group of Generalised Bas-Relief (GBR) transformations. We show that this ambiguity is resolved when specular reflection is present in two images taken under two different light source directions. We identify all configurations of the two directional lights which are singular and show that they can easily be tested for. While previous work used optimisation algorithms to apply the constraints implied by the specular reflectance component, we have developed a linear algorithm to achieve this goal. Our theory can be utilised to construct fast algorithms for automatic reconstruction of smooth glossy surfaces. 1

Detecting Shadows and Specularities by Moving Light

To detect and remove shadows and highlights is an important computer vision task. Specularities traveling on the surface represent one of the most serious hindrances to many stereo matching algorithms. Both shadows and highlights severely complicate the photometric stereo because they make the evaluation of normal directions impossible. The need to remove shadows and highlights is also important when one is about to create virtual environments and map texture extracted from images onto 3-D geometrical models. We present a global approach to this problem which is based on a set of input images of a scene illuminated from dierent directions. We show that under the assumption of Lambertian model of reectance, the intrinsic dimensionality of the input data is three. We rst apply the principal component analysis, demonstrate its non-robustness and show that extending the data set does not improve its performance. Results are shown for both synthetic and real images. Then we present a rob..