Multiview registration-based handheld 3D profiling system using visual navigation and structured light

Abstract

This article describes the 3D handheld profiling system composed of a stereo camera and an illumination projector to collect high-resolution data for close range of applications. Visual navigation approach is either based on feature matching or on accurate target, and the target-based approach was found to be more accurate if the 3D object has less texture on its surface. Block matching algorithm was used to render the single-view 3D reconstruction. For multiview 3D modeling, coarse registration and final refinement of the point clouds using iterative closest point algorithm were utilized. The proposed approach yields good accuracy for multiview registration as demonstrated in the results of this research.

User evaluation study on illumination requirements to design an augmented reality projector for open liver surgery

“…Finally, these technologies have failed to provide a solution to use the projector to visualize information on the patients under surgical lights. There are commercially available pico projectors that have good illuminance and luminous flux [23] but none of them match the illuminance provided by the surgical light.…”

High-Precision Calibration of a Monocular-Vision-Guided Handheld Line-Structured-Light Measurement System

“…Compared with the motional-axes-based line-structured-light measurement systems (LSLMS), the handheld measuring devices are more flexible. The objects can be scanned from different viewpoints to ensure measurement integrity [ 14 , 15 , 16 ], although the accuracy of the result highly relies on the data fusion techniques and has the problem of unsmooth edges [ 17 , 18 ]. Data fusion via marker points can lead to a higher accuracy and has been widely adopted by the commercial handheld scanners [ 19 ].…”

Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light

“…This problem was resolved by using structured light techniques [ 5 , 6 , 7 ] in which a projector (or projection system) replaces one of the cameras in the stereo pair and a coded pattern is projected onto the 3D object. User may not be able to acquire the complete 3D model from the modeling system in a single measurement step owing to self-occlusion and a limited field of view and requires merging multiple views into a complete 3D model [ 8 ]. To merge different range images, we need to align these scans with respect to a common coordinate system using the process known as registration.…”

“…Let us consider the handheld rotation of an object by a small angle, wherein the images of the object are captured by a fixed sensor; here the range images can be aligned using a refinement algorithm owing to the fixed camera coordinate system, whereas for the same rotation of handheld scanner, this assumption is not valid owing to large displacement of the object in two different views and to the change in camera coordinate system [ 9 ]. Hence the multiview registration approach to tackle handheld operation, consists of two stages, coarse registration and refinement using the iterative closest point (ICP) algorithm [ 8 , 9 ]. The coarse registration is needed to handle unstable handheld motion and especially for tackling large motion where ICP-based refinement does not perform well.…”