Invariant surface characteristics for 3D object recognition in range images
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Pose Determination of a Three-Dimensional Object Using Triangle Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Silhouette-based occluded object recognition through curvature scale space
Machine Vision and Applications
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric and Illumination Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
A Spherical Representation for Recognition of Free-Form Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential invariants as the base of triangulated surface registration
Computer Vision and Image Understanding - Registration and fusion of range images
Harmonic shape images: a three-dimensional free-form surface representation and its applications in surface matching
A Flexible Similarity Measure for 3D Shapes Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploration trees on highly complex scenes: A new approach for 3D segmentation
Pattern Recognition
3D free-form object recognition in range images using local surface patches
Pattern Recognition Letters
3D scene analysis from a single range image through occlusion graphs
Pattern Recognition Letters
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
The correspondence framework for 3D surface matching algorithms
Computer Vision and Image Understanding
Perceptually relevant and piecewise linear matching of silhouettes
Pattern Recognition
Directional histogram model for three-dimensional shape similarity
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Supervised classification of multiple view images in object space for seismic damage assessment
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
3D object retrieval via range image queries based on SIFT descriptors on panoramic views
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Consensus strategy for clustering using RC-images
Pattern Recognition
3D object retrieval via range image queries in a bag-of-visual-words context
The Visual Computer: International Journal of Computer Graphics
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The intention of the strategy proposed in this paper is to solve the object retrieval problem in highly complex scenes using 3D information. In the worst case scenario the complexity of the scene includes several objects with irregular or free-form shapes, viewed from any direction, which are self-occluded or partially occluded by other objects with which they are in contact and whose appearance is uniform in intensity/color. This paper introduces and analyzes a new 3D recognition/pose strategy based on DGI (Depth Gradient Images) models. After comparing it with current representative techniques, we can affirm that DGI has very interesting prospects.The DGI representation synthesizes both surface and contour information, thus avoiding restrictions concerning the layout and visibility of the objects in the scene. This paper first explains the key concepts of the DGI representation and shows the main properties of this method in comparison to a set of known techniques. The performance of this strategy in real scenes is then reported. Details are also presented of a wide set of experimental tests, including results under occlusion, performance with injected noise and experiments with cluttered scenes of a high level of complexity.