Salient Local 3D Features for 3D Shape Retrieval

In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other methods.Comment: Three-Dimensional Imaging, Interaction, and Measurement. Edited by Beraldin, J. Angelo; Cheok, Geraldine S.; McCarthy, Michael B.; Neuschaefer-Rube, Ulrich; Baskurt, Atilla M.; McDowall, Ian E.; Dolinsky, Margaret. Proceedings of the SPIE, Volume 7864, pp. 78640S-78640S-8 (2011). Conference Location: San Francisco Airport, California, USA ISBN: 9780819484017 Date: 10 March 201

Computational Methods for Shape Manipulation in generation : a literature review

In this paper we will present a state of the art of the descriptive and generative models for shape. We will present several different approaches for the manipulation of shape in computational systems: numerical models, graph models, descriptive models. This investigation will lead to a discussion regarding the use of these models for supporting the generation of shapes in the early phases of the design process.ANR GENIUS (TECHLOG-07-010

Histogram of distances for local surface description

3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance

Learning with Latent Representations of 3D Data: from Classical Methods to 3D Deep Learning

3D data contain rich information about the full geometry of objects or scenes. Learning tasks on them have always been considered as hard ones in the computer vision community due to their extreme high dimensionality. Hence, latent representations of 3D geometries are often used to lower the data dimensionality for better parameterization and easier computation. In this report, we make a brief review on those latent representations obtained via different methods including classical ones and the emerging neural learning-based ones. Furthermore, the nowadays widely used deep learning methods have also been more closely investigated regarding their applications on various 3D data formats. The possibility of combing those two kinds of methods has also been addressed

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Surface feature detection and description with applications to mesh matching

In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, such as photometric information. This is motivated by the growing need for matching and tracking photometric surfaces over temporal sequences, due to recent advancements in multiple camera 3D reconstruction. We propose a 3D feature detector (MeshDOG) and a 3D feature descriptor (MeshHOG) for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale. The descriptor is able to capture the local geometric and/or photometric properties in a succinct fashion. Moreover, the method is defined generically for any scalar function, e.g., local curvature. Results with matching rigid and non-rigid meshes demonstrate the interest of the proposed framework

Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

[EN] Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2020/1.Pérez, J.; Guardiola Garcia, JL.; Pérez Jiménez, AJ.; Perez-Cortes, J. (2020). Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM). Sensors. 20(22):1-16. https://doi.org/10.3390/s20226554S1162022Brosed, F. J., Aguilar, J. J., Guillomía, D., & Santolaria, J. (2010). 3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot. Sensors, 11(1), 90-110. doi:10.3390/s110100090Perez-Cortes, J.-C., Perez, A., Saez-Barona, S., Guardiola, J.-L., & Salvador, I. (2018). A System for In-Line 3D Inspection without Hidden Surfaces. Sensors, 18(9), 2993. doi:10.3390/s18092993Bi, Z. M., & Wang, L. (2010). 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