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.
Limitations The 2D and 2.5D representations are insufficient for general purpose applications. For example, in the case of vehicle damage detection, a vehicle may be photographed from an arbitrary view in order to indicate the damaged parts. Similarly, the 3D multi-view feature representation is unsuitable as it restricts the pose of the object to a limited set of views. Therefore, an exact 3D representation is preferred. Little work has been done to date on identifying the pose of an exact 3D model from a single 2D image. Huttenlocher and Ullman @cite_11 use a 3D model that contains the locations of edges. The edges contours identified in the 2D image are matched against the edges in the 3D model to calculate the pose of the object. The method has been implemented for simple 3D objects. However, it is unclear if this method will work well on objects with rounded surfaces without clearly identifiable edges.
@cite_11
2033554200
This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. The precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. The 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with state-of-the-art 2D object detectors.