Model-based 3D Object Recognition Research Articles

COMBINED MODEL-BASED 3D OBJECT RECOGNITION

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

Automatic model-based 3D object recognition by combining feature matching with tracking

We propose a vision-based robust automatic 3D object recognition, which provides object identification and 3D pose information by combining feature matching with tracking. For object identification, we propose a robust visual feature and a probabilistic voting scheme. An initial object pose is estimated using correlations between the model image and the 3D CAD model, which are predefined, and the homography, byproduct of the identification. In tracking, a Lie group formalism is used for robust and fast motion computation. Experimental results show that object recognition by the proposed method improves the recognition range considerably.

FREE-FORM 3D OBJECT RECOGNITION IN RANGE DATA USING WEAK CORRESPONDENCE BETWEEN LOCAL FEATURES

Model-Based 3D object recognition systems have a variety of potential applications, but widespread use of such systems has not occurred, due to a number of factors including the representational limitations of models. One historical limitation is the discriminatory representation of free-form objects. The system described in this paper recognizes free-form objects in dense range data acquired by a structured light rangefinder. Images and object models are represented as a network of salient segments which are then brought into correspondence until a reliable pose estimate is available. Experiments with a database of images and object models highlight the contributions of this system.

Model-Based Object Recognition Using Geometric Invariants of Points and Lines

In this paper, we derive new geometric invariants for structured 3D points and lines from single image under projective transform, and we propose a novel model-based 3D object recognition algorithm using them. Based on the matrix representation of the transformation between space features (points and lines) and the corresponding projected image features, new geometric invariants are derived via the determinant ratio technique. First, an invariant for six points on two adjacent planes is derived, which is shown to be equivalent to Zhu's result [1], but in simpler formulation. Then, two new geometric invariants for structured lines are investigated: one for five lines on two adjacent planes and the other for six lines on four planes. By using the derived invariants, a novel 3D object recognition algorithm is developed, in which a hashing technique with thresholds and multiple invariants for a model are employed to overcome the over-invariant and false alarm problems. Simulation results on real images show that the derived invariants remain stable even in a noisy environment, and the proposed 3D object recognition algorithm is quite robust and accurate.

Model-Based Classification of Quadric Surfaces

Model-based 3D object recognition systems using range imagery typically employ entirely data-driven procedures for segmentation and surface classification. However, some recognition environments may contain only objects whose surface types and parameters are known a priori and can therefore be exploited by the early-processing steps used in the recognition system. We propose a new suite of model-driven techniques for identification of quadric surfaces (cones, cylinders, and spheres) in segmented range imagery. The methods employ surface positions and surface normal estimates in combination with the known parameters of surfaces in a database of object models. Second-derivative quantities (i.e., surface curvatures) are not used. The free parameters of cylinders and spheres are accumulated using a Hough transform, and free parameters of cones are estimated using a regression procedure. Experiments are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, objects containing concave and convex surfaces, and noiseless and noisy synthetic range images of objects. Our experimental results show that the proposed surface classification methods can accurately recover surface parameters from both synthetic and real images, making them viable for environments with partial knowledge of surface type and parameters.

BONSAI: 3D object recognition using constrained search

BONSAI, a model-based 3D object recognition system, is described. It identifies and localizes 3D objects in range images of one or more parts that have been designed on a computer-aided-design (CAD) system. Recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. Attention is focused on the recognition procedure, but the model-building, image acquisition, and segmentation procedures are also outlined. Experiments with over 200 images demonstrate that the constrained search approach to 3D object recognition has an accuracy comparable to that of previous systems. >

Recognition and positioning of three-dimensional objects by combining matchings of primitive local patterns

A model-based 3D object recognition and positioning method is proposed. The proposed recognition method is based on the idea of combining many local matchings into a comprehensive and consistent interpretation of the scene: primitive local patterns in the scene are matched with those in the model by using the edge and surface information together, and the compatible local matchings are accumulated and combined to give several interpretations of the scene. The most appropriate interpretation is selected as the recognized object. In this paper a pair of adjacent facets is adopted as the primitive local pattern. The proposed method is robust to occlusion and works with only a partial view of the object. This method is also suitable for those objects which have very similar shapes when viewed from a particular direction. The method was tested on images of realistic 3D object scenes obtained with a laser range finder.