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

Geometric Reasoning With a Virtual Workforce (Crowdsourcing for CAD/CAM)

This paper reports the initial results of employing a commercial Crowdsourcing (aka Micro-outsourcing) service to provide geometric analysis of complex 3D models of mechanical components. Although Crowdsourcing sites (which distribute browser based tasks to potentially large numbers of anonymous workers on the Internet) are well established for image analysis and text manipulation there is little academic work on the effectiveness or limitations of the approach. The work reported here describes the initial results of using Crowdsourcing to determine the 'best' canonical, or characteristic, views of complex 3D models of engineering components. The results suggest that the approach is a cheap, fast and effective method of solving what is a computationally difficult problem