convex optimization; categorization; object detection; 3D reconstruction
Szabó Attila, Hu Qiyang, Portenier Tiziano, Zwicker Matthias, Favaro Paolo (2018), Understanding Degeneracies and Ambiguities in Attribute Transfer, in ECCV
, Munich, GermanySpringer International Publishing/IEEE/CVF, Munich, Germany.
Zwicker Matthias, Hu Qiyang, Szabo Attila, Portenier Tiziano, Favaro Paolo (2018), Disentangling Factors of Variation by Mixing Them, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
, Salt Lake City, UTIEEE/CVF, Salt Lake City, UT.
Szabo Attila, Vedaldi Andrea, Favaro Paolo (2015), Building the View Graph of a Category by Exploiting Image Realism, in IEEE International Conference on Computer Vision Workshop (ICCVW)
, IEEE ICCV, Santiago, Chile.
Object detection and categorization are fundamental problems in computer vision and the first steps towards building intelligent systems capable of interacting with our environment. Unfortunately, these are problems of baffling complexity due to the high dimensionality of visual data and its highly nonlinear dependency to nuisance factors such as viewpoint, scale, illumination, intra-class object variability, clutter, occlusions and noise.Inspired by recent approaches, we propose to investigate a novel detection and categorization approach focused on the 3D shape of objects. The novelty in our approach lies in how we learn the 3D model from data and how we use it for detection and categorization. During training we propose to build a 3D model of an object directly from images of a category. At runtime, we use 3D models at two levels: locally, to select valid 2D landmarks in images and, globally, to certify that the collection of selected 2D landmarks is the projection of a known 3D geometry. To deal with intra-class variability, a key ingredient in our approach is the design of feature descriptors invariant to texture variations. These descriptors are used to establish landmark correspondences both during training and testing. Textural information is only used at runtime for the category selection.