SYSTEMS AND METHODS FOR PITCH DETERMINATION
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.
1 . (canceled)
2 . A method of implemented by a system of one or more computers, the method comprising:
providing access to an image depicting a 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 vertical vector.
3 . The method of claim 2 , wherein a second neural network outputs the vertical vector.
4 . The method of claim 2 , further comprising identifying the vertical vector based on a vanishing point coordinate system.
5 . The method of claim 4 , wherein identifying the vertical vector based on the vanishing point coordinate system comprises:
determining the vanishing point coordinate system by extending lines associated with the structure to determine intersections of the lines associated with the structure; and
identifying the vertical vector based on a first axis of the vanishing point system.
6 . The method of claim 2 , further comprising determining the vertical vector based on a feature associated with the particular wall.
7 . The method of claim 2 , wherein the neural network comprises a convolutional neural network trained to segment the planar elements into at least the roof facet or the one or more walls.
8 . The method of claim 7 , 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.
9 . 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 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
extract a pitch of the roof facet based on the adjusted surface normal associated with the roof facet and a vertical vector.
10 . The system of claim 9 , wherein a second neural network outputs the vertical vector.
11 . The system of claim 9 , wherein the instructions further cause the one or more processors to identify the vertical vector based on a vanishing point coordinate system.
12 . The system of claim 11 , wherein to identify the vertical vector based on the vanishing point coordinate system, the instructions cause the one or more processors to:
determine the vanishing point coordinate system by extending lines associated with the structure to determine intersections of the lines associated with the structure; and
identify the vertical vector based on a first axis of the vanishing point system.
13 . The system of claim 9 , wherein the instructions further cause the one or more processors to determine the vertical vector based on a feature associated with the particular wall.
14 . The system of claim 9 , wherein the neural network comprises a convolutional neural network trained to segment the planar elements into at least the roof facet or the one or more walls.
15 . The system of claim 14 , 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.
16 . Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors to perform operations comprising:
providing access to an image depicting a 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 vertical vector.
17 . The non-transitory computer storage media of claim 16 , wherein a second neural network outputs the vertical vector.
18 . The non-transitory computer storage media of claim 16 , wherein the instructions further cause the one or more processors to perform operations comprising identifying the vertical vector based on a vanishing point coordinate system.
19 . The non-transitory computer storage media of claim 18 , identifying the vertical vector based on the vanishing point coordinate system comprises:
determining the vanishing point coordinate system by extending lines associated with the structure to determine intersections of the lines associated with the structure; and
identifying the vertical vector based on a first axis of the vanishing point system.
20 . The non-transitory computer storage media of claim 16 , wherein the neural network comprises a convolutional neural network trained to segment the planar elements into at least the roof facet or the one or more walls.
21 . The non-transitory computer storage media of claim 20 , 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.