Structured light illumination is an active 3D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface. As designed, each pixel in the camera sees exactly one pixel from the projector; however, there are multi-path situations when the scanned surface has a complicated geometry with step edges and other discontinuities in depth or where the target surface has specularities that reflect light away from the camera. These situations are generally referred to multi-path where a camera pixel sees light from multiple projector positions. In the case of bimodal multi-path, the camera pixel receives light from exactly two positions which occurs along a step edge where the edge slices through a pixel so that the pixel sees both a foreground and background surface. In this paper, we present a general mathematical model to address the bimodal multi-path issue in a phase-measuring-profilometry scanner to measure the constructive and destructive interference between the two light paths, and by taking advantage of this interesting cue, separate the paths and make two decoupled phase measurements. We validate our algorithm with a number of challenging real-world scenarios, outperforming the state-of-the-art method.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Number of pages||9|
|State||Published - Jun 2019|
|Event||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States|
Duration: Jun 16 2019 → Jun 20 2019
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Period||6/16/19 → 6/20/19|
Bibliographical noteFunding Information:
National Science Foundation under contract No. 1539157
© 2019 IEEE.
- 3D from Multiview and Sensors
- RGBD sensors and analytics
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition