The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods. It does not require prior training, knowledge of the camera parameters, explicit point correspondences or matching features between the image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object. It works on a single static image from a given view under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between the 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection in real photographs are presented.
2D representations store the information of a particular 2D view of an object (a characteristic view) as a model and use this information to identify the object from a 2D image. Global feature methods have been used by Gleason and Algin @cite_1 to identify objects like spanners and nuts on a conveyor belt. Such methods use features such as the area, perimeter, number of holes visible and other global features to model the object. Structural features like boundary segments have been used by Perkins @cite_13 to detect machine parts using 2D models. A relational graph method has been used by Yachida and Tsuji @cite_19 to match objects to a 2D model using graph matching techniques. These 2D representation based algorithms require prior training of the system using a show by example' method.
@cite_13 @cite_19 @cite_1
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Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of objects and 2D representations that can be robustly matched to natural images. Most approaches thus rely on 2D natural images either as the sole source of training data for building an implicit 3D representation, or by enriching 3D models with natural image features. In this paper, we go back to the ideas from the early days of computer vision, by using 3D object models as the only source of information for building a multi-view object class detector. In particular, we use these models for learning 2D shape that can be robustly matched to 2D natural images. Our experiments confirm the validity of our approach, which outperforms current state-of-the-art techniques on a multi-view detection data set. According to the 1.5-views theorem (Poggio, Technical Report #9005-03, IRST, Povo, 1990; Ullman and Basri, IEEE Trans. PAMI 13, 992-1006, 1991) recognition of a specific 3D object (defined in terms of pointwise features) from a novel 2D view can be achieved from at least two 2D model views (for each object, for orthographic projection). This note considers how recognition can be achieved from a single 2D model view by exploiting prior knowledge of an object's symmetry. It is proved that, for any bilaterally symmetric 3D object, one non-accidental 2D model view is sufficient for recognition since it can be used to generate additional 'virtual' views. It is also proved that, for bilaterally symmetric objects, the correspondence of four points between two views determines the correspondence of all other points. Symmetries of higher order allow the recovery of Euclidean structure from a single 2D view.1 While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.