Robust and Fast 3-D Saliency Mapping for Industrial Modeling Applications

Abstract

New generation 3D scanning technologies are expected to create a revolution at the Industry 4.0, facilitating a large number of virtual manufacturing tools and systems. Such applications require the accurate representation of physical objects and/or systems achieved through saliency estimation mechanisms that identify certain areas of the 3D model, leading to a meaningful and easier to analyze representation of a 3D object. 3D saliency mapping is, therefore, guiding the selection of feature locations and is adopted in a large number of low-level 3D processing applications including denoising, compression, simplification and registration. In this work, we propose a robust and fast method for creating 3D saliency maps that accurately identifies sharp and small scale geometric features in various industrial 3D models. An extensive experimental study using a large number of 3D scanned and CAD models, verifies the effectiveness of the proposed method as compared to other recent and relevant approaches despite the constraints posed by complex geometry patterns or the presence of noise. Index Terms-3D Mesh saliency mapping, industrial modeling & applications, spectral & geometric analysis for vertex saliency.

Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces

“…Recently, Sinha et al developed Object Shape Error Response (OSER) for single-station [47] [5] and Object Shape Error Response for Multi-Station Assembly Systems (OSER-MAS) [6] that aim to integrate Bayesian deep learning elements such as Bayesian 3D Convolutional Neural Networks and Computer-Aided Engineering (CAE) simulations thereby, blending (a) engineering knowledgetechniques with (b) estimation-based data-driven approaches. This satisfies various model capability requirements for RCA of MASs such as (i) high data dimensionality [48]; (ii) nonlinearity [49]; (iii) collinearities [50]; (iv) high faults multiplicity [51]; (v) uncertainty quantification [52]; (vi) dual data generation capabilities [12]; (vii) high dimensionality and heterogeneity of process parameters [53]; and, (viii) fault localization [54]. In addition to the aforementioned model capability requirements, RCA techniques must be further developed and enhanced to fulfil two additional key requirements [55] in order to enable implementation and large-scale adoption across different manufacturing environments: (ix) Scalability as automotive multi-station assembly processes include hundreds of stamping parts and components, multiple stations with multiple stages in each station [4] namely, place-clamp-fasten-release (PCFR) to finish the final assembly product.…”

Cooperative Saliency-Based Pothole Detection and AR Rendering for Increased Situational Awareness

“…2) Point Cloud Saliency: One of main challenges in techniques utilizing point clouds is the inherent noise and the increased computational cost due to the unordered data structure of point clouds. To address such challenges, saliency map extraction has been proposed as a powerful step in point cloud processing to reduce noise and data dimensionality, leading to more robust solutions and computational efficiency [31], [32]. Yet, the use of local saliency in pothole detection has not been sufficiently examined.…”

“…For the estimation of the saliency map, we implemented and modified the fusion technique presented in [32]. Instead of using guided normals of centroids, as in the original version [32], we now utilize normals for the points. This was performed to accelerate computations.…”

Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts

“…At the end of the final assembly stage = 4 the object shape error data for the assembly is collected and decomposed into the nominal points and their deviations by using alignment techniques [9], where , are now a collective reference to the set of all incoming objects that have been assembled. The measurement system error is considered to be negligible ( ≈ 0).…”

“…(i) High data dimensionality of a batch of 3D objects [9] which are defined by CAD (ideal parts) and point-clouds (nonideal parts) with millions of points for each part or subassembly.…”