While texture analysis is largely addressed for images, the comparison of the geometric reliefs on surfaces embedded in the 3D space is still an open challenge. Starting from the Local Binary Pattern (LBP) description originally defined for images, we introduce the edge-Local Binary Pattern (edgeLBP) as a local description able to capture the evolution of repeated, geometric patterns on surface meshes. Our extension is independent of the surface representation, indeed the edgeLBP is able to deal with surface tessellations characterized by non-uniform vertex distributions and different types of faces, such as triangles, quadrangles and, in general, convex polygons. Besides the desirable robustness properties the edgeLBP exhibits over a number of examples, we show how this description performs well for 3D pattern retrieval and compare our performances with the participants to a recent 3D pattern retrieval and classification contest.
* 3D pattern retrieval Methods in the literature for shape matching and retrieval can be classified according to their type of input, their local or global nature, their robustness to noise and model representations, their invariance to shape transformations and their suitability to partial matching @cite_45 @cite_30 @cite_3 . For a detailed overview of algorithms and methods, we refer to recent surveys @cite_39 @cite_30 @cite_3 and to the proceedings of the annual SHape REtrieval Contest http: www.shrec.net event. Here, we focus on methods that are potentially able to address the retrieval of 3D patterns, i.e., methods that are defined for surfaces, adopt a local shape description, are able to detected repeated features, and are independent of rigid transformations of the 3D models.
@cite_30 @cite_45 @cite_3 @cite_39
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Complex geometric structural variations of 3D model usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a high-level shape feature learning scheme to extract features that are insensitive to deformations via a novel discriminative deep auto-encoder. First, a multiscale shape distribution is developed for use as input to the auto-encoder. Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning. Finally, the neurons in the hidden layers from multiple discriminative auto-encoders are concatenated to form a shape descriptor for 3D shape matching and retrieval. The proposed method is evaluated on the representative datasets that contain 3D models with large geometric variations, i.e., Mcgill and SHREC'10 ShapeGoogle datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method for 3D shape matching and retrieval. The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words” and treat them using text search approaches following the “bag of features” paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors by means of the “bag of features” approach. We also show that considering pairs of “geometric words” (“geometric expressions”) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark. Our previous shape-based 3D model retrieval algorithm compares 3D shapes by using thousands of local visual features per model. A 3D model is rendered into a set of depth images, and from each image, local visual features are extracted by using the Scale Invariant Feature Transform (SIFT) algorithm by Lowe. To efficiently compare among large sets of local features, the algorithm employs bag-of-features approach to integrate the local features into a feature vector per model. The algorithm outperformed other methods for a dataset containing highly articulated yet geometrically simple 3D models. For a dataset containing diverse and detailed models, the method did only as well as other methods. This paper proposes an improved algorithm that performs equal or better than our previous method for both articulated and rigid but geometrically detailed models. The proposed algorithm extracts much larger number of local visual features by sampling each depth image densely and randomly. To contain computational cost, the method utilizes GPU for SIFT feature extraction and an efficient randomized decision tree for encoding SIFT features into visual words. Empirical evaluation showed that the proposed method is very fast, yet significantly outperforms our previous method for rigid and geometrically detailed models. For the simple yet articulated models, the performance was virtually unchanged. Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval methods have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2].