Systems and methods for surface modeling using polarization cues

Patent metadata

Field	Value
Publication number	US-11699273-B2
Application number	US-202217586666-A
Country	US
Kind code	B2
Filing date	Jan 27, 2022
Priority date	Sep 17, 2019
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

A computer-implemented method for surface modeling includes: receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured with a polarizing filter at different linear polarization angles; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces.

First claim

What is claimed is:

1. A computer-implemented method for surface modeling, the method comprising:
receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured at different polarizations by a polarization camera comprising a polarizing filter;
extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and
detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces, comprising:
loading a stored model corresponding to a location of the surface of the physical object, the stored model comprising one or more reference tensors in the one or more polarization representation spaces; and
computing the surface characteristic in accordance with the stored model and the one or more first tensors in the one or more polarization representation spaces comprising computing a difference between the one or more reference tensors and the one or more first tensors in the one or more polarization representation spaces.

2. The computer-implemented method of claim 1 , wherein the one or more first tensors in the one or more polarization representation spaces comprise:
a degree of linear polarization (DOLP) image in a DOLP representation space; and
an angle of linear polarization (AOLP) image in an AOLP representation space.

3. The computer-implemented method of claim 1 , wherein the one or more first tensors further comprise one or more non-polarization tensors in one or more non-polarization representation spaces, and
wherein the one or more non-polarization tensors comprise one or more intensity images in intensity representation space.

4. The computer-implemented method of claim 3 , wherein the one or more intensity images comprise:
a first color intensity image;
a second color intensity image; and
a third color intensity image.

5. The computer-implemented method of claim 1 , wherein the surface characteristic comprises a detection of a defect in the surface of the physical object.

6. The computer-implemented method of claim 1 , wherein the difference is computed using a Fresnel distance.

7. The computer-implemented method of claim 1 , wherein the stored model comprises a trained statistical model configured to compute a prediction of the surface characteristic based on the one or more first tensors in the one or more polarization representation spaces.

8. The computer-implemented method of claim 7 , wherein the trained statistical model comprises an anomaly detection model.

9. The computer-implemented method of claim 7 , wherein the trained statistical model comprises a convolutional neural network trained to detect defects in the surface of the physical object.

10. The computer-implemented method of claim 7 , wherein the trained statistical model comprises a trained classifier trained to detect defects.

11. A system for surface modeling, the system comprising:
a polarization camera comprising a polarizing filter, the polarization camera being configured to capture polarization raw frames at different polarizations; and
a processing system comprising a processor and memory storing instructions that, when executed by the processor, cause the processor to:
receive one or more polarization raw frames of a surface of a physical object, the polarization raw frames corresponding to different polarizations of light;
extract one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and
detect a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces, the instructions further comprising instructions that, when executed by the processor, cause the processor to detect the stored characteristic by:
loading a stored model corresponding to a location of the surface of the physical object, the stored model comprising one or more reference tensors in the one or more polarization representation spaces; and
computing the surface characteristic in accordance with the stored model and the one or more first tensors in the one or more polarization representation spaces by computing a difference between the one or more reference tensors and the one or more first tensors in the one or more polarization representation spaces.

12. The system of claim 11 , wherein the one or more first tensors in the one or more polarization representation spaces comprise:
a degree of linear polarization (DOLP) image in a DOLP representation space; and
an angle of linear polarization (AOLP) image in an AOLP representation space.

13. The system claim 11 , wherein the one or more first tensors further comprise one or more non-polarization tensors in one or more non-polarization representation spaces, and wherein the one or more non-polarization tensors comprise one or more intensity images in intensity representation space.

14. The system of claim 13 , wherein the one or more intensity images comprise:
a first color intensity image;
a second color intensity image; and
a third color intensity image.

15. The system of claim 11 , wherein the surface characteristic comprises a detection of a defect in the surface of the physical object.

16. The system of claim 11 , wherein the difference is computed using a Fresnel distance.

17. The system of claim 11 , wherein the stored model comprises a trained statistical model configured to compute a prediction of the surface characteristic based on the one or more first tensors in the one or more polarization representation spaces.

18. The system of claim 17 , wherein the trained statistical model comprises an anomaly detection model.

19. The system of claim 17 , wherein the trained statistical model comprises a convolutional neural network trained to detect defects in the surface of the physical object.

20. The system of claim 17 , wherein the trained statistical model comprises a trained classifier trained to detect defects.

21. One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
receiving one or more polarization raw frames of a surface of a physical object, the polarization raw frames being captured at different polarizations by a polarization camera comprising a polarizing filter;
extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and
detecting a surface characteristic of the surface of the physical object based on the one or more first tensors in the one or more polarization representation spaces, comprising:
loading a stored model corresponding to a location of the surface of the physical object, the stored model comprising one or more reference tensors in the one or more polarization representation spaces; and
computing the surface characteristic in accordance with the stored model and the one or more first tensors in the one or more polarization representation spaces comprising computing a difference between the one or more reference tensors and the one or more first tensors in the one or more polarization representation spaces.

Assignees

• Intrinsic Innovation Llc

Inventors

• Kadambi Achuta
• Kalra Agastya
• Rao Supreeth Krishna
• Venkataraman Kartik

Classifications

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V2201/06

  Recognition of objects for industrial automation · CPC title

• G06V20/80 Primary

  Recognising image objects characterised by unique random patterns · CPC title

• G06V10/60

  relating to illumination properties, e.g. using a reflectance or lighting model · CPC title

• G06V10/82

  using neural networks · CPC title