View-based 3-D object retrieval and recognition has become popular in practice, e.g., in computer aided design. It is difficult to precisely estimate the distance between two objects represented by multiple views. Thus, current view-based 3-D object retrieval and recognition methods may not perform well. In this paper, we propose a hypergraph analysis approach to address this problem by avoiding the estimation of the distance between objects. In particular, we construct multiple hypergraphs for a set of 3-D objects based on their 2-D views. In these hypergraphs, each vertex is an object, and each edge is a cluster of views. Therefore, an edge connects multiple vertices. We define the weight of each edge based on the similarities between any two views within the cluster. Retrieval and recognition are performed based on the hypergraphs. Therefore, our method can explore the higher order relationship among objects and does not use the distance between objects. We conduct experiments on the National Taiwan University 3-D model dataset and the ETH 3-D object collection. Experimental results demonstrate the effectiveness of the proposed method by comparing with the state-of-the-art methods.

3-D Object Retrieval and Recognition With Hypergraph Analysis

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.

3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains. Unsupervised domain adaptation aims to solve the real-world problem of domain shift, where machine learning models trained on one domain must be transferred and adapted to a novel visual domain without additional supervision. The VisDA2017 challenge is focused on the simulation-to-reality shift and has two associated tasks: image classification and image segmentation. The goal in both tracks is to first train a model on simulated, synthetic data in the source domain and then adapt it to perform well on real image data in the unlabeled test domain. Our dataset is the largest one to date for cross-domain object classification, with over 280K images across 12 categories in the combined training, validation and testing domains. The image segmentation dataset is also large-scale with over 30K images across 18 categories in the three domains. We compare VisDA to existing cross-domain adaptation datasets and provide a baseline performance analysis using various domain adaptation models that are currently popular in the field.

VisDA: The Visual Domain Adaptation Challenge

In this article, we present the Yale-Carnegie Mellon University (CMU)-Berkeley (YCB) object and model set, intended to be used to facilitate benchmarking in robotic manipulation research. The objects in the set are designed to cover a wide range of aspects of the manipulation problem. The set includes objects of daily life with different shapes, sizes, textures, weights, and rigidities as well as some widely used manipulation tests. The associated database provides high-resolution red, green, blue, plus depth (RGB-D) scans, physical properties, and geometric models of the objects for easy incorporation into manipulation and planning software platforms. In addition to describing the objects and models in the set along with how they were chosen and derived, we provide a framework and a number of example task protocols, laying out how the set can be used to quantitatively evaluate a range of manipulation approaches, including planning, learning, mechanical design, control, and many others. A comprehensive literature survey on the existing benchmarks and object data sets is also presented, and their scope and limitations are discussed. The YCB set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences. Subsequent sets will be, otherwise, available to purchase at a reasonable cost. It is our hope that the ready availability of this set along with the ground laid in terms of protocol templates will enable the community of manipulation researchers to more easily compare approaches as well as continually evolve standardized benchmarking tests and metrics as the field matures.

Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set

Most methods for the recognition of shape classes from 3D datasets focus on classifying clean, often manually generated models. However, 3D shapes obtained through acquisition techniques such as Structure-from-Motion or LIDAR scanning are noisy, clutter and holes. In that case global shape features--still dominating the 3D shape class recognition literature--are less appropriate. Inspired by 2D methods, recently researchers have started to work with local features. In keeping with this strand, we propose a new robust 3D shape classification method. It contains two main contributions. First, we extend a robust 2D feature descriptor, SURF, to be used in the context of 3D shapes. Second, we show how 3D shape class recognition can be improved by probabilistic Hough transform based methods, already popular in 2D. Through our experiments on partial shape retrieval, we show the power of the proposed 3D features. Their combination with the Hough transform yields superior results for class recognition on standard datasets. The potential for the applicability of such a method in classifying 3D obtained from Structure-from-Motion methods is promising, as we show in some initial experiments.

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Hough transform and 3D SURF for robust three dimensional classification

In this paper, we present an evaluation strategy based on human-generated ground truth to measure the performance of 3D interest point detection techniques. We provide quantitative evaluation measures that relate automatically detected interest points to human-marked points, which were collected through a web-based application. We give visual demonstrations and a discussion on the results of the subjective experiments. We use a voting-based method to construct ground truth for 3D models and propose three evaluation measures, namely False Positive and False Negative Errors, and Weighted Miss Error to compare interest point detection algorithms.

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Evaluation of 3D interest point detection techniques via human-generated ground truth

Hand biometrics

In this paper, we present an extensive study of 3D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules and voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we have tried to find the answers to the following questions: 1) the relative importance of the face representation techniques vis-a-vis the types of features extracted; 2) the impact of the gallery size; 3) the conditions, under which subspace methods are preferable, and the compression factor; 4) the most advantageous fusion level and fusion methods; 5) the role of confidence votes in improving fusion and the style of selecting experts in the fusion; and 6) the consistency of the conclusions across different databases.

Representation Plurality and Fusion for 3-D Face Recognition

We provide a survey of hand biometric techniques in the literature and incorporate several novel results of hand-based per- sonal identification and verification. We compare several feature sets in the shape-only and shape-plus-texture categories, empha- sizing the relevance of a proper hand normalization scheme in the success of any biometric scheme. The preference of the left and right hands or of ambidextrous access control is explored. Since the business case of a biometric device partly hinges on the longevity of its features and the generalization ability of its database, we have tested our scheme with time-lapse data as well as with subjects that were unseen during the training stage. Our experiments were con- ducted on a hand database that is an order of magnitude larger than any existing one in the literature. © 2008 SPIE and IS&T.

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Comparative analysis of global hand appearance-based person recognition

The best view selection corresponds to the task of automatically selecting the most representative view of a 3D model. In this paper, we describe a benchmark for evaluation of best view selection algorithms. The benchmark consists of the preferred views of 68 3D models provided by 26 human subjects. The data was collected using a web-based subjective experiment where the users were asked to select the most informative view of a 3D model. We provided a quantitative evaluation measure based on this ground truth data, and compared the performances of seven best view selection algorithms.

Helin Dutagaci

Papers

Evaluation of 3D interest point detection techniques via human-generated ground truth

Hand biometrics

Representation Plurality and Fusion for 3-D Face Recognition

Comparative analysis of global hand appearance-based person recognition

A benchmark for best view selection of 3D objects