Novel metrics and methodology for the characterisation of 3D imaging systems

© 2016 The AuthorsThe modelling, benchmarking and selection process for non-contact 3D imaging systems relies on the ability to characterise their performance. Characterisation methods that require optically compliant artefacts such as matt white spheres or planes, fail to reveal the performance limitations of a 3D sensor as would be encountered when measuring a real world object with problematic surface finish. This paper reports a method of evaluating the performance of 3D imaging systems on surfaces of arbitrary isotropic surface finish, position and orientation. The method involves capturing point clouds from a set of samples in a range of surface orientations and distances from the sensor. Point clouds are processed to create a single performance chart per surface finish, which shows both if a point is likely to be recovered, and the expected point noise as a function of surface orientation and distance from the sensor. In this paper, the method is demonstrated by utilising a low cost pan-tilt table and an active stereo 3D camera. Its performance is characterised by the fraction and quality of recovered data points on aluminium isotropic surfaces ranging in roughness average (Ra) from 0.09 to 0.46 µm at angles of up to 55° relative to the sensor over a distances from 400 to 800 mm to the scanner. Results from a matt white surface similar to those used in previous characterisation methods contrast drastically with results from even the dullest aluminium sample tested, demonstrating the need to characterise sensors by their limitations, not just best case performance