The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. 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 neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between 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, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.
Model based object recognition has received considerable attention in computer vision circles. A survey by Chin and Dyer @cite_18 shows that model based object recognition algorithms generally fall into 3 categories, based on the type of object representation used - namely 2D representations, 2.5D representations or 3D representations.
@cite_18
2026311529
This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.