Multi-modal depth mapping

Patent metadata

Field	Value
Publication number	US-10636155-B2
Application number	US-201816180039-A
Country	US
Kind code	B2
Filing date	Nov 5, 2018
Priority date	Jul 30, 2009
Publication date	Apr 28, 2020
Grant date	Apr 28, 2020

Abstract

A method for depth mapping includes acquiring first depth data with respect to an object using a first depth mapping technique and providing first candidate depth coordinates for a plurality of pixels, and acquiring second depth data with respect to the object using a second depth mapping technique, different from the first depth mapping technique, and providing second candidate depth coordinates for the plurality of pixels. A weighted voting process is applied to the first and second depth data in order to select one of the candidate depth coordinates at each pixel. A depth map of the object is output, including the selected one of the candidate depth coordinates at each pixel.

First claim

The invention claimed is:

1. A method for depth mapping, comprising:
acquiring first depth data with respect to an object using a first depth mapping technique and providing first candidate depth coordinates for a plurality of pixels;
acquiring second depth data with respect to the object using a second depth mapping technique, different from the first depth mapping technique, and providing second candidate depth coordinates for the plurality of pixels,
wherein acquiring the first and second depth data comprises computing respective measures of confidence associated with the first and second candidate depth coordinates;
applying a weighted voting process to the first and second depth data in order to select one of the candidate depth coordinates at each pixel,
wherein applying the weighted voting process comprises weighting votes for the candidate depth coordinates responsively to the respective measures of confidence; and
outputting a depth map of the object comprising the selected one of the candidate depth coordinates at each pixel.

2. The method according to claim 1 , wherein the first and second candidate depth coordinates comprise, for at least some of the pixels, a null coordinate indicating that no valid depth coordinate was found.

3. The method according to claim 1 , wherein applying the weighted voting process comprises applying weighted tensor voting among the pixels in order to select the one of the candidate depth coordinates based on the candidate depth coordinates at neighboring pixels.

4. The method according to claim 3 , wherein applying the weighted tensor voting comprises computing a weighted sum of covariance matrices over the neighboring pixels, and selecting the one of the candidate depth coordinates based on a difference between eigenvalues of the summed covariance matrices.

5. The method according to claim 1 , wherein at least one of the first and second depth mapping techniques comprises pattern-based depth mapping.

6. The method according to claim 1 , wherein at least one of the first and second depth mapping techniques comprises stereoscopic depth mapping.

7. The method according to claim 6 , wherein the first depth mapping technique comprises pattern-based depth mapping, while the second depth mapping technique comprises stereoscopic depth mapping, and
wherein acquiring the first depth data comprises projecting a pattern of optical radiation onto the object, capturing a first image of the pattern on the object using a first image sensor, and processing the first image to generate pattern-based depth data with respect to the object, and
wherein acquiring the second depth data comprises capturing a second image of the object using a second image sensor, and processing the second image together with the first image to generate stereoscopic depth data with respect to the object.

8. Apparatus for depth mapping, comprising:
one or more sensors, which are configured to acquire first depth data with respect to an object using a first depth mapping technique and providing first candidate depth coordinates for a plurality of pixels, and to acquire second depth data with respect to the object using a second depth mapping technique, different from the first depth mapping technique, and providing second candidate depth coordinates for the plurality of pixels; and
a processor, which is configured to apply a weighted voting process to the first and second depth data in order to select one of the candidate depth coordinates at each pixel, and to output a depth map of the object comprising the selected one of the candidate depth coordinates at each pixel,
wherein the processor is configured to associate respective measures of confidence with the first and second candidate depth coordinates, and to weight votes for the candidate depth coordinates responsively to the respective measures of confidence.

9. The apparatus according to claim 8 , wherein the first and second candidate depth coordinates comprise, for at least some of the pixels, a null coordinate indicating that no valid depth coordinate was found.

10. The apparatus according to claim 8 , wherein the processor is configured to apply weighted tensor voting among the pixels in order to select the one of the candidate depth coordinates based on the candidate depth coordinates at neighboring pixels.

11. The apparatus according to claim 10 , wherein the processor is configured to compute a weighted sum of covariance matrices over the neighboring pixels, and to select the one of the candidate depth coordinates based on a difference between eigenvalues of the summed covariance matrices.

12. The apparatus according to claim 8 , wherein at least one of the first and second depth mapping techniques comprises pattern-based depth mapping.

13. The apparatus according to claim 8 , wherein at least one of the first and second depth mapping techniques comprises stereoscopic depth mapping.

14. The apparatus according to claim 13 , wherein the first depth mapping technique comprises pattern-based depth mapping, while the second depth mapping technique comprises stereoscopic depth mapping, and wherein the apparatus comprises:
an illumination subassembly, which is configured to project a pattern of optical radiation onto an object;
a first image sensor, which is configured to capture a first image of the pattern on the object; and
at least a second image sensor, which is configured to capture at least a second image of the object,
wherein the processor is configured to process the first image to generate pattern-based depth data with respect to the object and to process the second image together with the first image to generate stereoscopic depth data with respect to the object.

15. A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a processor, cause the processor to acquire first depth data with respect to an object using a first depth mapping technique and providing first candidate depth coordinates for a plurality of pixels, and to acquire second depth data with respect to the object using a second depth mapping technique, different from the first depth mapping technique, and providing second candidate depth coordinates for the plurality of pixels, to apply a weighted voting process to the first and second depth data in order to select one of the candidate depth coordinates at each pixel, and to output a depth map of the object comprising the selected one of the candidate depth coordinates at each pixel,
wherein the instructions cause the processor to associate respective measures of confidence with the first and second candidate depth coordinates, and to weight votes for the candidate depth coordinates responsively to the respective measures of confidence.

16. The product according to claim 15 , wherein the first and second candidate depth coordinates comprise, for at least some of the pixels, a null coordinate indicating that no valid depth coordinate was found.

17. The product according to claim 15 , wherein the instructions cause the processor to apply weighted tensor voting among the pixels in order to select the one of the candidate depth coordinates based on the candidate depth coordinates at neighboring pixels.

Assignees

• Apple Inc

Inventors

• Shpunt Alexander
• Medioni Gerard
• Cohen Daniel
• Sali Erez
• Deitch Ronen

Classifications

• G06T2207/10024

  Color image · CPC title

• H04N13/271

  wherein the generated image signals comprise depth maps or disparity maps · CPC title

• G06T7/521 Primary

  from laser ranging, e.g. using interferometry; from the projection of structured light · CPC title

• H04N13/25

  using two or more image sensors with different characteristics other than in their location or field of view, e.g. having different resolutions or colour pickup characteristics; using image signals from one sensor to control the characteristics of another sensor · CPC title

• H04N13/254

  in combination with electromagnetic radiation sources for illuminating objects · CPC title