3D Object Recognition Based on Image Features: A Survey

2004

This paper addresses the problem of recognizing three-dimensional (3D) objects in photographs and image sequences. It revisits viewpoint invariants as a local representation of shape and appearance, and proposes a unified framework for object recognition where object models consist of a collection of small (planar) patches, their invariants, and a description of their 3D spatial relationship. This approach is applied to two fundamental instances of the 3D object recognition problem: (1) modeling rigid 3D objects from a small set of unregistered pictures and recognizing them in cluttered photographs taken from unconstrained viewpoints, and (2) recognizing non-uniform texture patterns despite appearance variations due to non-rigid transformations and changes in viewpoint. It is validated through several experiments, and extensions to the analysis of video sequences and the recognition of object categories are briefly discussed.

This paper synthesizes insights from recent research on 3D object detection and recognition in digital images. The surveyed literature showcases advancements in deep learning architectures, sensor fusion techniques, real-time processing, robustness to occlusion, and domain adaptation. Notably, the integration of point cloud data in deep learning models enhances accuracy, while sensor fusion improves reliability in diverse lighting conditions. Optimized real-time processing, multi-view systems, and domain adaptation methods address specific challenges, contributing to the field's progress. Standard metrics and benchmark evaluations validate the effectiveness of proposed methodologies, highlighting their potential for real-world applications.

2006

We present a method for 3D object modeling and recognition which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived from the local features extracted and tracked on an image sequence of the object. The recognition phase is based on an SVM classifier. We analyse in depth all the crucial steps of the method, and report very promising results on a dataset of 11 objects, that show how the method is also tolerant to occlusions and moderate scene clutter.

2000

The recognition of objects is one of the most challenging goals in computer vision. The problems increase when the process of recognition involved three dimensional (3D) objects. To deal with this problem, many researchers have proposed their own solution. This paper gives a short review of some of the researches in the area in representing their 3D models. It is

We propose techniques for designing and training of pose-invariant object recognition systems using realistic 3d computer graphics models. We look at the relation between the size of the training set and the classification accuracy for a basic recognition task and provide a method for estimating the degree of difficulty of detecting an object. We show how to sample, align, and cluster images of objects on the view sphere. We address the problem of training on large, highly redundant data and propose a novel active learning method which generates compact training sets and compact classifiers.

Pattern Recognition, 2005

In this article we present a new appearance-based approach for the classification and the localization of 3-D objects in complex scenes. A main problem for object recognition is that the size and the appearance of the objects in the image vary for 3-D transformations. For this reason, we model the region of the object in the image as well as the object features themselves as functions of these transformations. We integrate the model into a statistical framework, and so we can deal with noise and illumination changes. To handle heterogeneous background and occlusions, we introduce a background model and an assignment function. Thus, the object recognition system becomes robust, and a reliable distinction, which features belong to the object and which to the background, is possible. Experiments on three large data sets that contain rotations orthogonal to the image plane and scaling with together more than 100 000 images show that the approach is well suited for this task.

1995

A probabilistic 3D object recognition algorithm is presented. In order to guide the recognition process the probability that match hypotheses between image features and model features are correct is computed. A model is developed which uses the probabilistic peaking e ect of measured angles and ratios of lengths by tracing iso-angle and iso-ratio curves on the viewing sphere. The model also accounts for various types of uncertainty in the input such as incomplete and inexact edge detection. For each match hypothesis the pose of the object and the pose uncertainty which is due to the uncertainty in vertex position are recovered. This is used to nd sets of hypotheses which reinforce each other by matching features of the same object with compatible uncertainty regions. A probabilistic expression is used to rank these hypothesis sets. The hypothesis sets with the highest rank are output. The algorithm has been fully implemented, and tested on real images.

Image and Vision Computing, 1992

This paper concerns a topics studied by many researchers around the world: 3D object recognition from vision. In our robotics context,an object must be recognized and localized in order to be grasped by a mobile robot equipped with a manipulator arm Mitsubishi PA10-6C: several cameras are mounted on this robot, on a static mast or on the wrist of the arm. The use of such a robot for object recognition, makes possible active strategies for object recognition. This system must be able to place the sensor in different positions around the object in order to learn discriminant features on every object to be recognized in a first step, and then to recognize these objects before a grasping task. Our method exploits the Mutual Information to actively acquire visual data until the recognition, like it was proposed in works presented in (Trujillo-Romero et al. 2004) and (Denzler et al., 2001): color histogram, shape context, shape signature ,interest points harris et sift descriptors are learnt from different viewpoint around every object in order to make the system more robust and efficient.

A simple but very efficient technique of 3D object reconstruction and recognition is presented here. An optical and computer vision based measurement system is used to acquire the object's data. The recognition algorithm is a distance calculator. It is used to compute the proximity of the object's shape under test and a reference object's geometry shape. Here, as an application example, we presents a human face recognition case. The effectiveness of the algorithm is demonstrated here in two tests in which the same person is compared with himself within different facial expressions and with others persons with different faces expressions too. The results are presented in a comparative chart.