Systems and methods for pitch determination

Patent metadata

Field	Value
Publication number	US-12243163-B2
Application number	US-202418441974-A
Country	US
Kind code	B2
Filing date	Feb 14, 2024
Priority date	Aug 26, 2020
Publication date	Mar 4, 2025
Grant date	Mar 4, 2025

Abstract

Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

First claim

What is claimed is:

1. A method implemented by a system of one or more computers, the method comprising:
providing access to an image depicting a structure, the image being captured via a user device positioned proximate to the structure, the structure having a plurality of planar elements comprising at least a roof facet and one or more walls;
providing the image as input to a neural network, wherein the neural network outputs, at least, a surface normal associated with the roof facet and a surface normal associated with a particular wall of the one or more walls;
adjusting each surface normal based on a transform, wherein the transform adjusts at least the surface normal associated with the particular wall to be substantially orthogonal to a vertical orientation; and
extracting a pitch of the roof facet based on the adjusted surface normal associated with the roof facet and a gravity vector.

2. The method of claim 1 , wherein the neural network comprises a convolutional neural network trained to segment the planar elements into at least the roof facet or the wall.

3. The method of claim 2 , wherein the neural network further comprises one or more fully-connected layers which receive output from the convolutional neural network, and wherein the fully-connected layers are trained to output individual surface normals associated with individual planar elements.

4. The method of claim 1 , wherein the image was captured below a height of the structure.

5. The method of claim 1 , further comprising determining a plurality of surface normals corresponding to a plurality of roof facets.

6. The method of claim 1 , wherein the gravity vector is a unit vector oriented along the vertical orientation.

7. The method of claim 1 , further comprising determining the gravity vector based on a feature associated with the particular wall.

8. The method of claim 1 , wherein the one or more walls include a plurality of walls, and wherein the neural network outputs a surface normal associated with each of the walls.

9. The method of claim 8 , wherein the transform adjusts the surface normal associated with each of the walls to be substantially orthogonal to vertical.

10. The method of claim 8 , wherein the transform adjusts individual surface normals associated with a subset of the walls to be substantially orthogonal to vertical.

11. A system comprising one or more processors and non-transitory computer readable media storing instructions which, when executed by the one or more processors, cause the one or more processors to:
provide access to an image depicting a structure, the image being captured via a user device positioned proximate to the structure, the structure having a plurality of planar elements comprising at least a roof facet and one or more walls;
provide the image as input to a neural network, wherein the neural network outputs, at least, a surface normal associated with the roof facet and a surface normal associated with a particular wall of the one or more walls;
adjust each surface normal based on a transform, wherein the transform adjusts at least the surface normal associated with the particular wall to be substantially orthogonal to a vertical orientation; and
extracting a pitch of the roof facet based on the adjusted surface normal associated with the roof facet and a gravity vector.

12. The system of claim 11 , wherein the neural network comprises a convolutional neural network trained to segment the planar elements into at least the roof facet or the wall.

13. The system of claim 12 , wherein the neural network further comprises one or more fully-connected layers which receive output from the convolutional neural network, and wherein the fully-connected layers are trained to output individual surface normals associated with individual planar elements.

14. The system of claim 11 , wherein the image was captured below a height of the structure.

15. The system of claim 11 , wherein the instructions further cause the one or more processors to determine a plurality of surface normals corresponding to a plurality of roof facets.

16. The system of claim 11 , wherein the gravity vector is a unit vector oriented along the vertical orientation.

17. The system of claim 11 , wherein the gravity vector is based on a feature associated with the particular wall.

18. The system of claim 11 , wherein the one or more walls include a plurality of walls, and wherein the neural network outputs a surface normal associated with each of the walls.

19. The system of claim 18 , wherein the transform adjusts the surface normal associated with each of the walls to be substantially orthogonal to vertical.

20. The system of claim 18 , wherein the transform adjusts individual surface normals associated with a subset of the walls to be substantially orthogonal to vertical.

Assignees

• Hover Inc

Inventors

• Castillo William
• Murali Giridhar
• Scott Brandon
• Jia Kai
• Sommers Jeffrey
• Rethage Dario

Classifications

• G06T7/70

  Determining position or orientation of objects or cameras (camera calibration G06T7/80) · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06N3/08

  Learning methods · CPC title

• G06T2207/20092

  Interactive image processing based on input by user · CPC title

• G06T7/10

  Segmentation; Edge detection (motion-based segmentation G06T7/215) · CPC title