This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.

/pdf/unique-signatures-of-histograms-for-local-surface-dbxl9xe6lq.pdf

Unique signatures of histograms for local surface description

This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme. The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together. Such representation allows using much sparser object and scene point clouds, resulting in very fast performance. Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space. We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusions. Compared to state of the art approaches we achieve better recognition rates, and demonstrate that with a slight or even no sacrifice of the recognition performance our method is much faster then the current state of the art approaches.

/pdf/model-globally-match-locally-efficient-and-robust-3d-object-4hksf8mv37.pdf

Model globally, match locally: Efficient and robust 3D object recognition

SHOT: Unique signatures of histograms for surface and texture description

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

3D free-form object recognition in range images using local surface patches

Human ear is a new class of relatively stable biometrics that has drawn researchers' attention recently. In this paper, we propose a complete human recognition system using 3D ear biometrics. The system consists of 3D ear detection, 3D ear identification, and 3D ear verification. For ear detection, we propose a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix parts in registered 2D color and 3D range images. For ear identification and verification using range images, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation computed at feature points. A local surface descriptor is characterized by a centroid, a local surface type, and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values versus the angles between the normal of reference feature point and that of its neighbors. Both shape representations are used to estimate the initial rigid transformation between a gallery-probe pair. This transformation is applied to selected locations of ears in the gallery set and a modified iterative closest point (ICP) algorithm is used to iteratively refine the transformation to bring the gallery ear and probe ear into the best alignment in the sense of the least root mean square error. The experimental results on the UCR data set of 155 subjects with 902 images under pose variations and the University of Notre Dame data set of 302 subjects with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of the proposed algorithms and the system

Human Ear Recognition in 3D

Ear is a new class of relatively stable biometric that is invariant from childhood to early old age (8 to 70). It is not affected with facial expressions, cosmetics and eye glasses. In this paper, we introduce a two-step ICP (Iterative Closest Point) algorithm for matching 3D ears. In the first step, the helix of the ear in 3D images is detected. The ICP algorithm is run to find the initial rigid transformation to align a model ear helix with the test ear helix. In the second step, the initial transformation is applied to selected locations of model ears and the ICP algorithm iteratively refines the transformation to bring model ears and test ear into best alignment. The root mean square (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear. Experimental results on a dataset of 30 subjects with 3D ear images are presented to demonstrate the effectiveness of the approach.

Contour Matching for 3D Ear Recognition

Most existing work in 3D object recognition in computer vision has been on recognizing dissimilar objects using a small database. For rapid indexing and recognition of highly similar objects, this paper proposes a novel method which combines the feature embedding for the fast retrieval of surface descriptors, novel similarity measures for correspondence and a support vector machine (SVM)-based learning technique for ranking the hypotheses. The local surface patch (LSP) representation is used to find the correspondences between a model-test pair. Due to its high dimensionality, an embedding algorithm is used that maps the feature vectors to a low-dimensional space where distance relationships are preserved. By searching the nearest neighbors in low dimensions, the similarity between a model-test pair is computed using the novel features. The similarities for all model-test pairs are ranked using the learning algorithm to generate a short list of candidate models for verification. The verification is performed by aligning a model with the test object. The experimental results, on the UND dataset (302 subjects with 604 images) and the UCR dataset (155 subjects with 902 images) that contain 3D human ears, are presented and compared with the geometric hashing technique to demonstrate the efficiency and effectiveness of the proposed approach.

/pdf/efficient-recognition-of-highly-similar-3d-objects-in-range-4b467euh3c.pdf

Efficient Recognition of Highly Similar 3D Objects in Range Images

Ear detection is an important part of an ear recognition system In this paper, we address human ear detection from side face range images We introduce a simple and an effective method to detect ears, which has two stages: offline model template building and on-line detection The model template is represented by an averaged histogram of shape index The on-line detection is a four-step process: step edge detection and thresholding, image dilation, connect-component labeling and template matching Experimental results with real ear images are presented to demonstrate the effectiveness of our approach

http://www.vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Conference/2004/Human%20Ear%20Detection%20from%20Side%20Face04.pdf

Hui Chen

Papers

3D free-form object recognition in range images using local surface patches

Human Ear Recognition in 3D

Contour Matching for 3D Ear Recognition

Efficient Recognition of Highly Similar 3D Objects in Range Images

Human ear detection from side face range images