Geometry and Procedure for Benchmarking SFF and Hybrid Fabrication Process Resolution

Since the advent of SFF and RP a number of SFF benchmarking geometries and methodologies have been developed and employed with some similarities but limited standardization. Minimal information has been published in regard to a standard method of measuring the resolution limits or capabilities of SFF and SFF-based hybrid processes. In an effort to benchmark resolution limits of SFF and Hybrid Fabrication processes, several benchmarking geometries were developed to capture the resolution capabilities, specifically hole size and rod size range, of multiple hybrid fabrication path steps and a hybrid path as a whole. These useful geometries are shared with the SFF community and procedures for their use are described in this paper.Mechanical Engineerin

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.Comment: CVPR 2018. The first two authors contributed equally to this work. Project page: http://pix3d.csail.mit.ed

Construction informatics in Turkey: strategic role of ICT and future research directions

Construction Informatics deals with subjects ranging from strategic management of ICTs to interoperability and information integration in the construction industry. Studies on defining research directions for Construction Informatics have a history over 20 years. The recent studies in the area highlight the priority themes for Construction Informatics research as interoperability, collaboration support, intelligent sites and knowledge sharing. In parallel, today it is widely accepted in the Architecture/Engineering/Construction (AEC) industry that ICT is becoming a strategic asset for any organisation to deliver business improvement and achieve sustainable competitive advantage. However, traditionally the AEC industry has approached investing in ICT with a lack of strategic focus and low level of priority to the business. This paper presents a recent study from Turkey that is focused on two themes. The first theme investigates the strategic role of ICT implementations from an industrial perspective, and explores if organisations within the AEC industry view ICT as a strategic resource for their business practice. The second theme investigates the ‘perspective of academia’ in terms of future research directions of Construction Informatics. The results of the industrial study indicates that ICT is seen as a value-adding resource, but a shift towards the recognition of the importance of ICT in terms of value adding in winning work and achieving strategic competitive advantage is observed. On the other hand, ICT Training is found to be the theme of highest priority from the academia point of view

Construction informatics in Turkey: strategic role of ICT and future research directions

Construction Informatics deals with subjects ranging from strategic management of ICTs to interoperability and information integration in the construction industry. Studies on defining research directions for Construction Informatics have a history over 20 years. The recent studies in the area highlight the priority themes for Construction Informatics research as interoperability, collaboration support, intelligent sites and knowledge sharing. In parallel, today it is widely accepted in the Architecture/Engineering/Construction (AEC) industry that ICT is becoming a strategic asset for any organisation to deliver business improvement and achieve sustainable competitive advantage. However, traditionally the AEC industry has approached investing in ICT with a lack of strategic focus and low level of priority to the business. This paper presents a recent study from Turkey that is focused on two themes. The first theme investigates the strategic role of ICT implementations from an industrial perspective, and explores if organisations within the AEC industry view ICT as a strategic resource for their business practice. The second theme investigates the ‘perspective of academia’ in terms of future research directions of Construction Informatics. The results of the industrial study indicates that ICT is seen as a value-adding resource, but a shift towards the recognition of the importance of ICT in terms of value adding in winning work and achieving strategic competitive advantage is observed. On the other hand, ICT Training is found to be the theme of highest priority from the academia point of view

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Geometric reasoning via internet crowdsourcing

The ability to interpret and reason about shapes is a peculiarly human capability that has proven difficult to reproduce algorithmically. So despite the fact that geometric modeling technology has made significant advances in the representation, display and modification of shapes, there have only been incremental advances in geometric reasoning. For example, although today's CAD systems can confidently identify isolated cylindrical holes, they struggle with more ambiguous tasks such as the identification of partial symmetries or similarities in arbitrary geometries. Even well defined problems such as 2D shape nesting or 3D packing generally resist elegant solution and rely instead on brute force explorations of a subset of the many possible solutions. Identifying economic ways to solving such problems would result in significant productivity gains across a wide range of industrial applications. The authors hypothesize that Internet Crowdsourcing might provide a pragmatic way of removing many geometric reasoning bottlenecks.This paper reports the results of experiments conducted with Amazon's mTurk site and designed to determine the feasibility of using Internet Crowdsourcing to carry out geometric reasoning tasks as well as establish some benchmark data for the quality, speed and costs of using this approach.After describing the general architecture and terminology of the mTurk Crowdsourcing system, the paper details the implementation and results of the following three investigations; 1) the identification of "Canonical" viewpoints for individual shapes, 2) the quantification of "similarity" relationships with-in collections of 3D models and 3) the efficient packing of 2D Strips into rectangular areas. The paper concludes with a discussion of the possibilities and limitations of the approach