Geometric reasoning via internet CrowdSourcing

Abstract

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.

Outsourcing labour to the cloud

“…The classification of 3D shapes might appear an easy task to humans, but it is an almost impossible task to perform by a machine. The difficulty in defining, and therefore cataloguing complex shapes or textures has caused engineering companies (Jagadeesan et al, 2009a;Jagadeesan et al, 2009b) to turn to crowdsourcing to be able to classify their stock. In this way, they find a more efficient way to search in their stores for a specific piece or part required in the assembly instead of re-drawing and manufacturing again from scratch, as done traditionally because the search of the part in the store proved to be more tedious and time-consuming that its remanufacture.…”

An evaluation methodology for crowdsourced design

“…These are generous payment levels in comparison to other reported research studies which could as low as $0.01 [35], $0.10 [36] [35]. However, in consideration of the experiment's level of difficulty, the lowest rate was fixed as $0.15 [37] [38]. By choosing $1.00 as a maximum payment the task would by one of the best paid on the platform where only some translation jobs might be paid as much as $1.40 per hour [35] [39].…”

A novel hybrid intelligence approach for 2D packing through internet crowdsourcing

“…The authors of [11] have used crowdsourcing for spatial manufacturing tasks. The authors of [12] have applied the geometrical reasoning task through the crowdsourcing. The authors in [13] and [14] have used combination of human and machine intelligence in the domains of business prediction and classifying celestial bodies respectively.…”

“…Solving 2D packing problems through crowdsourcing, particularly by rural people, will give a greater impact on the industrial applications [12] and also benefit rural people as large population of India live in villages. The packing problem becomes easier when both objects and the containing region are regular/ rectangular in shape, but in most of the practical situations, the irregular shapes have to be used.…”