This paper presents a multiview model of object categories generally applicable to virtually any type of image features and methods to efficiently perform in a unified manner detection localization and continuous pose estimation in novel scenes . We represent appearance as distributions of low level fine grained image features . Multiview models encode the appearance of objects at discrete viewpoints and in addition how these viewpoints deform into one another as the viewpoint continuously varies . Using a measure of similarity between an arbitrary test image and such a model at chosen viewpoints we perform all tasks mentioned above with a common method . We leverage the simplicity of low level image features such as points extracted along edges or coarse scale gradients extracted densely over the images by building probabilistic templates i.e . distributions of features learned from one or several training examples . We efficiently handle these distributions with probabilistic techniques such as kernel density estimation Monte Carlo integration and importance sampling . We provide an extensive evaluation on a wide variety of benchmark datasets . We demonstrate performance on the ETHZ Shape dataset with single and multiple training examples well above baseline methods on par with a number of more task specific methods . We obtain remarkable performance on the recognition of more complex objects notably the cars of the 3D Object dataset of Savarese et al . with detection rates of 92.5 and an accuracy in pose estimation of 91 . We perform better than the state of the art on continuous pose estimation with the rotating cars dataset of Ozuysal et al . We also demonstrate particular capabilities with a novel dataset featuring non textured objects of undistinctive shapes the pose of which can only be determined from shading captured here by coarse scale intensity gradients . This paper is concerned with the joint recognition and pose estimation of object categories in 2D images . Recognizing that these two tasks represent two sides of a same problem we tackle them in a unified approach . In general the pose of objects can not be inferred from just one type of image information e.g . silhouette and edges to cite a common example . Additional visual cues may be necessary such as the shading onto the object surface . A key point of our contributions is thus to provide techniques generally applicable in this regard even to low level dense and or non descriptive image features . To perform continuous pose estimation our object model captures in addition to the appearance at discrete training viewpoints the deformations between these detected from the optical flow between training examples . A measure of similarity between generated views of the object and a test image allows us to perform detection recognition and pose estimation in a unified manner . The following paragraphs present the principal motivations and key points of the method comparing them to existing related work . Parts of these contributions were introduced in earlier publications . The. 


Summary: Multi view model of object categories. Suitable to any type of image features e.g. edges and coarse scale gradients here. Performs detection localization and continuous pose estimation in unified manner. Encode appearance at discrete training viewpoints and in between. Competitive with best task specific methods with framework generally applicable.