Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.
|Title of host publication||Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021|
|Number of pages||10|
|State||Published - 2021|
|Event||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States|
Duration: Jun 19 2021 → Jun 25 2021
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021|
|Period||6/19/21 → 6/25/21|
Bibliographical noteFunding Information:
This paper has elucidated the problem of multi-path in the structured light method. The problem of mixing multiple rays in one pixel was formulated as an under-determined linear system. Furthermore, a mathematical method for estimating the contained light rays under the assumption of sparseness was described. The proposed modified version of the sparse Bayesian learning method to handle constraints is potentially useful for other problems as well. As we have demonstrated, the new technique outperforms the bimodal multi-path solution when dealing with sharp edges, inter-reflection, or semi-transparent objects. Moreover, it was shown that the number of separable paths depends on the frequency set. In the future, we may seek to obtain some ideas from  and try computing the SVD of Φ constructed by different frequencies to investigate the optimal frequency setting for sparse multi-path corrections. Acknowledgments The project was partially supported by the Open Research Fund of State Key Laboratory of Transient Optics and Photonics.
© 2021 IEEE
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition