Crosstalk reduction for intra-oral scanning using patterned light
According to some embodiments there is provided a method for structured light scanning of an intra-oral scene, comprising: projecting onto the intra-oral scene a color-coded pattern comprising an arrangement of entities having edges between them; each entity comprising a different narrow band of wavelengths; and detecting the projected pattern as a plurality of pixels in an acquired image of the scene using at least two narrowband filters, wherein for each pixel of at least 95% of the pixels of an entity of interest comprising a first band of wavelengths, a contribution of light from a second band of wavelengths of an adjacent entity is less than 10%. Some embodiments relate to a scanner system for structured light scanning of an intra-oral scene.
1. A structured light scanning method for structured light scanning of an intra-oral scene, comprising:
projecting onto an intra-oral scene a color-coded pattern comprising color entities separated from each other by dark regions;
acquiring an image of the intra-oral scene; and
determining one or more depths in said intra-oral scene based on:
detecting said projected pattern in the acquired image of said scene;
differentiating color entities in the acquired image using color filters; and
subtracting color obtained in an adjacent dark region from color obtained in a color entity of interest, thereby reducing crosstalk.
2. The method of claim 1 , wherein said intraoral scene comprises teeth.
3. The method of claim 1 , wherein said subtracting comprises subtracting colored light of at least one projected colored entity which is scattered through a tooth and emerges back to said image at a spatial location of a different projected colored entity.
4. The method of claim 1 , wherein said dark regions are sized to reduce crosstalk.
5. The method according to claim 1 , wherein said acquiring is performed using a color imager having a Bayer filter.
6. The method according to claim 1 , wherein said detecting comprises determining colors in the imaged pattern using said dark regions as reference.
7. The method according to claim 1 , wherein said pattern comprises colored stripes separated from each other by dark regions in the form of stripes.
8. The method according to claim 1 , wherein said determining one or more depths comprises indexing said color entities of said pattern, and wherein said dark regions define entities of the pattern which can be indexed in addition to said color entities.
9. The method of claim 1 , further comprising restoring said projected pattern by associating an imaged entity to a projected entity by identifying the wavelength band of said projected entity in said image.
10. The method of claim 9 , further comprising differentiating between said color entities by separating between at least two spectral bands wherein at least one of said spectral bands is a narrow band wherein said differentiating is performed using at least two narrowband filters.
11. The method claim 10 , wherein at least one of said at least two narrowband filters captures at least 95% of the pixels of said at least one spectral band.
12. The method according to claim 1 , further comprising:
constructing a 3D model of the scene using inner-image information obtained from said image acquired under unstructured lighting and inner-image information obtained from said image acquired under said patterned lighting; and
said constructing comprises identifying borders of smooth patches in the scene, wherein:
said borders indicative of depth discontinuities, and
said smooth patches are indexed in a continuous manner.
13. The method of claim 1 and further comprising:
imaging said projected pattern as a plurality of pixels in an acquired image of said scene using a hyperspectral imager positioned to image said scene, wherein said hyperspectral imager comprises interference filters attached to pixels of said hyperspectral imager;
estimating the color of each pixel for associating said pixel with said projected color-coded pattern; and
determining depth in said scene according to said associating.
14. The method of claim 4 , wherein crosstalk is reduced by sizing said dark regions so as to avoid geometrical overlap between the imaged entities.
15. The method of claim 1 , wherein said pattern comprises parallel stripes.
16. The method of claim 1 , wherein said dark region comprises a non-illuminated area.
17. The method of claim 12 , wherein said unstructured lighting comprises uniform lighting;
wherein information obtained from said image acquired under unstructured lighting is used for one or more of:
coloring of the reconstructed scene,
assessment of a geometry of the scene,
evaluation of reflection characteristics of contents of the scene, and
assessment of locations prone to loss of a projected pattern entities.
18. The method according to claim 5 , wherein a sensor array of said Bayer filter includes increased numbers of blue pixels.
19. The method according to claim 1 , wherein said pattern comprises one or more calibration element for quantifying, from said image, one or more optical characteristic of one or both of:
said intra-oral scene; and an imager performing said acquiring.
20. The method according to claim 1 , wherein said differentiating comprises estimating a scattering factor of a local material and subtracting scattering according to said scattering factor.
21. The method according to claim 1 , wherein said subtracting comprises subtracting a weighted averages of colors obtained in said adjacent dark region, where weighting of said averages is according to a distance from said color entity of interest.
22. The method according to claim 1 , wherein said subtracting comprises subtracting ambient illumination.