3D object pose form clustering with multiple views

A candidate pose algorithm is described which computes object pose from an assumed correspondence between a pair of 2D image points and a pair of 3D model points. By computing many pose candidates actual object pose can usually be determined by detecting a cluster in the space of all candidates. Cluster space can receive candidate pose parameters from independent computations in different camera views. It is shown that use of of geometric constraint can be sufficient for reliable pose detection, but use of other knowledge, such as edge presence and type, can be easily added for increased efficiency.     

3D object recognition technique using multiple 2D views for Arabic sign language

Some objects in specific poses cannot be distinguished using a single view. A model is proposed and developed for 3D object recognition based on multiple-views; it was applied on hand postures recognition. A pulse-coupled neural network is used to generate features vector for single view. Two views with different view angles are used; each view generates its features’ vector. The two 2D-vectors are then linearly combined into one 3D vector. The hand postures are then combined to construct a dynamic gesture (word). The reconstruction is performed using best-match search algorithm. The experiment was conducted on 50 words and the result was 96% recognition accuracy confirming objects dataset offline extendibility.     

Automatic 3D object segmentation in multiple views using volumetric graph-cuts

We propose an algorithm for automatically obtaining a segmentation of a rigid object in a sequence of images that are calibrated for camera pose and intrinsic parameters. Until recently, the best segmentation results have been obtained by interactive methods that require manual labelling of image regions. Our method requires no user input but instead relies on the camera fixating on the object of interest during the sequence. We begin by learning a model of the object’s colour, from the image pixels around the fixation points. We then extract image edges and combine these with the object colour information in a volumetric binary MRF model. The globally optimal segmentation of 3D space is obtained by a graph-cut optimisation. From this segmentation an improved colour model is extracted and the whole process is iterated until convergence.     

Adaptive 3-D object recognition from multiple views

The authors address the problem of generating representations of 3-D objects automatically from exploratory view sequences of unoccluded objects. In building the models, processed frames of a video sequence are clustered into view categories called aspects, which represent characteristic views of an object invariant to its apparent position, size, 2-D orientation, and limited foreshortening deformation. The aspects as well as the aspect transitions of a view sequence are used to build (and refine) the 3-D object representations online in the form of aspect-transition matrices. Recognition emerges as the hypothesis that has accumulated the maximum evidence at each moment. The `winning' object continues to refine its representation until either the camera is redirected or another hypothesis accumulates greater evidence. This work concentrates on 3-D appearance modeling and succeeds under favorable viewing conditions by using simplified processes to segment objects from the scene and derive the spatial agreement of object features     

3D object recognition based on curvature information of planar views

In this paper, a 3D object recognition algorithm is proposed. Objects are recognized by studying planar images corresponding to a sequence of views. Planar shape contours are represented by their adaptively calculated curvature functions, which are decomposed in the Fourier domain as a linear combination of a set of representative shapes. Finally, sequences of views are identified by means of Hidden Markov Models. The proposed system has been tested for artificial and real objects. Distorted and noisy versions of the objects were correctly clustered together.     

Robust 3D object registration without explicit correspondence using geometric integration

3D vision-guided manipulation of components is a key problem of industrial machine vision. In this paper, we focus on the localization and pose estimation of known industrial objects from 3D measurements delivered by a scanning sensor. Since local information extracted from these measurements is unreliable due to noise, spatially unstructured measurements and missing detections, we present a novel objective function for robust registration without using correspondence information, based on the likelihood of model points. Furthermore, by extending Runge–Kutta-type integration directly to the group of Euclidean transformation, we infer object pose by computing the gradient flow directly on the related manifold. Comparison of our approach to existing state of the art methods shows that our method is more robust against poor initializations while having comparable run-time performance.     

Invariant object recognition in the visual system with novel views of 3D objects

To form view-invariant representations of objects, neurons in the inferior temporal cortex may associate together different views of an object, which tend to occur close together in time under natural viewing conditions. This can be achieved in neuronal network models of this process by using an associative learning rule with a short-term temporal memory trace. It is postulated that within a view, neurons learn representations that enable them to generalize within variations of that view. When three-dimensional (3D) objects are rotated within small angles (up to, e.g., 30 degrees), their surface features undergo geometric distortion due to the change of perspective. In this article, we show how trace learning could solve the problem of in-depth rotation-invariant object recognition by developing representations of the transforms that features undergo when they are on the surfaces of 3D objects. Moreover, we show that having learned how features on 3D objects transform geometrically as the object is rotated in depth, the network can correctly recognize novel 3D variations within a generic view of an object composed of a new combination of previously learned features. These results are demonstrated in simulations of a hierarchical network model (VisNet) of the visual system that show that it can develop representations useful for the recognition of 3D objects by forming perspective-invariant representations to allow generalization within a generic view.     

应用多个正交视角轮流逼近3维目标的坐标

为实现目标的快速、精确3维定位和跟踪,提出一种正交摄像机视频定位系统及其坐标轮流逼近的迭代定位算法。系统中平面摄像机光轴按照正交方式布置,拍摄方向均指向原点。不同于现有的大部分计算机视觉方法,本算法中没有影响定位效率和精度问题的图像配准操作。证明了迭代算法的收敛性。数值验证和实际试验表明,本算法计算简单、误差稳定性好,收敛快,因此具有良好的应用潜力。     

Stereo VoVNet-CNN for 3D object detection

Multimedia Tools and Applications - 3D object detection is a key issue and research in autonomous vehicle and computer vision. 3D detection methods based on stereoscopic images estimate 3D boxes...     

Registration of 3D objects and surfaces

An approach to the problem of comparative analysis of objects and their surfaces that arises in medical imaging is presented and illustrated with an application to postsurgical bone-graft separation. A common requirement in medical imaging applications is the registration of multiple representations of the object prior to mensuration. The modeling of the registration process and the registration system are described. The measurement of change in volume of bone grafts implanted inside the human body in a corrective surgical procedure is discussed, and the results are evaluated     

Silhouette and stereo fusion for 3D object modeling

In this paper, we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multi-stereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multi-grid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the contour generators of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.     

Providing views and closure for the object data management group object model

The object data management group (ODMG) object model offers a standard for object-oriented database designers, while attempting to address some issues of interoperability. This research is focused on the viability of using the ODMG data model as a canonical data model in a multidatabase environment, and where weaknesses are identified we have proposed amendments to enable the model to suit the specific needs of this type of distributed database system. This paper describes our efforts to extend its relational style algebra, and to provide query closure and a viewing mechanism for object query language to construct multidatabase schemas.     

Model-based 3D object detection

Fast detection of objects in a home or office environment is relevant for robotic service and assistance applications. In this work we present the automatic localization of a wide variety of differently shaped objects scanned with a laser range sensor from one view in a cluttered setting. The daily-life objects are modeled using approximated Superquadrics, which can be obtained from showing the object or another modeling process. Detection is based on a hierarchical RANSAC search to obtain fast detection results and the voting of sorted quality-of-fit criteria. The probabilistic search starts from low resolution and refines hypotheses at increasingly higher resolution levels. Criteria for object shape and the relationship of object parts together with a ranking procedure and a ranked voting process result in a combined ranking of hypothesis using a minimum number of parameters. The experimental evaluation of the method and experiments from cluttered table top scenes demonstrate the effectiveness and robustness of the approach, feasible for real world object localization and robot grasp planning.     

3D object recognition method with multiple feature extraction from LiDAR point clouds

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.

     

Off-the-shelf CNN features for 3D object retrieval

Multimedia Tools and Applications - Effective feature representation is crucial to view-based 3D object retrieval (V3OR). Most previous works employed hand-crafted features to represent the views...     

View-wised discriminative ranking for 3D object retrieval

In this paper, we propose a new framework which can capture the latent relative information within the multiple views of 3D model, named View-wised Discriminative Ranking(VDR). Different to existing view-based methods which treat the multiple views as the independent information, we want to model the relative information within multiple views. By placing the views of model in certain order, we learn the parameters of ranking function as a new robust model representation. We evaluate our proposal on several challenging datasets for 3D retrieval and the comparison experiments demonstrate the superiority of the proposed method in both retrieval accuracy and efficiency.