PROVIDING AND/OR ANALYZING THREE-DIMENSION MODELS OF INFRASTRUCTURE ASSETS

1 .- 16 . (canceled)

17 . A computer-implemented method comprising acts of:

using a plurality of images captured by a moving camera to generate a point cloud;

identifying, from the point cloud, a plurality of points corresponding to an infrastructure asset;

using the plurality of points to estimate at least one plane;

generating at least one synthetic image of the infrastructure asset, the at least one synthetic image having at least one camera pose perpendicular to the at least one plane; and

using the at least one synthetic image to assess at least one condition of the infrastructure asset.

18 . The computer-implemented method of claim 17 , wherein:

the method further comprises an act of estimating a pose of the moving camera; and

the at least one camera pose of the at least one synthetic image is different from the pose of the moving camera.

19 . The computer-implemented method of claim 18 , wherein:

the infrastructure asset comprises a sign; and

the method further comprises an act of using the pose of the moving camera and geospatial metadata associated with at least one of the plurality of images to geolocate the sign.

20 . The computer-implemented method of claim 17 , wherein:

the method further comprises acts of:

applying a segmentation model to identify, from at least one image captured by the moving camera, a plurality of pixels corresponding to the infrastructure asset; and

mapping the plurality of pixels to the plurality of points.

21 . The computer-implemented method of claim 17 , wherein:

the infrastructure asset comprises a pavement surface; and

identifying a plurality of points corresponding to an infrastructure asset comprises identifying a plurality of points having surface normals that are within a selected threshold of vertical.

22 . The computer-implemented method of claim 17 , wherein:

the infrastructure asset comprises a pavement surface; and

identifying a plurality of points corresponding to an infrastructure asset comprises identifying a plurality of points having pixel colors that match one or more selected pavement colors.

23 . The computer-implemented method of claim 17 , wherein:

the point cloud is georeferenced based on geospatial metadata associated with the plurality of images; and

the selected camera pose comprises a selected georeferenced position.

24 . The computer-implemented method of claim 17 , wherein:

the infrastructure asset comprises a pavement surface of a road segment;

the at least one synthetic image comprises a plurality of synthetic images; and

the at least one selected camera pose comprises a plurality of selected camera poses distributed along the road segment, the plurality of selected camera poses having a same height and a same orientation relative to the road segment.

25 . The computer-implemented method of claim 17 , wherein:

the infrastructure asset comprises a pavement surface comprising a plurality of non-overlapping intervals;

the at least one synthetic image comprises a plurality of synthetic images; and

each synthetic image of the plurality of synthetic images shows a respective interval of the plurality of non-overlapping intervals.

26 . The computer-implemented method of claim 17 , further comprising acts of:

estimating a motion of the camera; and

sampling, based on the estimated motion of the camera, from a video stream captured by the camera, the plurality of images used to generate the point cloud.

27 . The computer-implemented method of claim 26 , wherein:

the video stream is sampled at a first frequency in response to determining that the estimated motion of the camera includes rotational motion; and

the video stream is sampled at a second frequency in response to determining that the estimated motion of the camera does not include rotational motion, wherein the second frequency is lower than the first frequency.

28 . The computer-implemented method of claim 17 , wherein:

using the at least one synthetic image to assess at least one condition of the infrastructure asset comprises applying at least one machine learning model to the at least one synthetic image.

29 . The computer-implemented method of claim 28 , wherein:

the at least one machine learning model is configured to provide an output selected from a group consisting of:

an assessment score indicating an extent of damage exhibited by the infrastructure asset;

a recommended type of maintenance for the infrastructure asset; and

a plurality of pixels likely to present damage of a selected type.

30 . A system comprising:

at least one processor; and

at least one computer-readable storage medium having stored thereon instructions which, when executed, program the at least one processor to:

use a plurality of images captured by a moving camera to generate a point cloud;

identify, from the point cloud, a plurality of points corresponding to an infrastructure asset;

use the plurality of points to estimate at least one plane;

generate at least one synthetic image of the infrastructure asset, the at least one synthetic image having at least one camera pose perpendicular to the at least one plane; and

use the at least one synthetic image to assess at least one condition of the infrastructure asset.

31 . The system of claim 30 , wherein:

the at least one processor is further programmed to estimate a pose of the moving camera; and

the at least one camera pose of the at least one synthetic image is different from the pose of the moving camera.

32 . The system of claim 31 , wherein:

the infrastructure asset comprises a sign; and

the at least one processor is further programmed to use the pose of the moving camera and geospatial metadata associated with at least one of the plurality of images to geolocate the sign.

33 . The system of claim 30 , wherein:

the at least one processor is further programmed to:

apply a segmentation model to identify, from at least one image captured by the moving camera, a plurality of pixels corresponding to the infrastructure asset; and

map the plurality of pixels to the plurality of points.

34 . The system of claim 30 , wherein:

the infrastructure asset comprises a pavement surface; and

the at least one processor is programmed to identify a plurality of points corresponding to the infrastructure asset at least in part by identifying a plurality of points having surface normals that are within a selected threshold of vertical.

35 . The system of claim 30 , wherein:

the infrastructure asset comprises a pavement surface; and

the at least one processor is programmed to identify a plurality of points corresponding to the infrastructure asset at least in part by identifying a plurality of points having pixel colors that match one or more selected pavement colors.

36 . The system of claim 30 , wherein:

the point cloud is georeferenced based on geospatial metadata associated with the plurality of images; and

the selected camera pose comprises a selected georeferenced position.

37 . The system of claim 30 , wherein:

the infrastructure asset comprises a pavement surface of a road segment;

the at least one synthetic image comprises a plurality of synthetic images; and

the at least one selected camera pose comprises a plurality of selected camera poses distributed along the road segment, the plurality of selected camera poses having a same height and a same orientation relative to the road segment.

38 . The system of claim 30 , wherein:

the infrastructure asset comprises a pavement surface comprising a plurality of non-overlapping intervals;

the at least one synthetic image comprises a plurality of synthetic images; and

each synthetic image of the plurality of synthetic images shows a respective interval of the plurality of non-overlapping intervals.

39 . The system of claim 30 , wherein:

the at least one processor is further programmed to:

estimate a motion of the camera; and

sample, based on the estimated motion of the camera, from a video stream captured by the camera, the plurality of images used to generate the point cloud.

40 . The system of claim 39 , wherein:

the video stream is sampled at a first frequency in response to determining that the estimated motion of the camera includes rotational motion; and

the video stream is sampled at a second frequency in response to determining that the estimated motion of the camera does not include rotational motion, wherein the second frequency is lower than the first frequency.

41 . The system of claim 30 , wherein:

the at least one processor is programmed to use the at least one synthetic image to assess at least one condition of the infrastructure asset at least in part by applying at least one machine learning model to the at least one synthetic image.

42 . The system of claim 41 , wherein:

the at least one machine learning model is configured to provide an output selected from a group consisting of:

an assessment score indicating an extent of damage exhibited by the infrastructure asset;

a recommended type of maintenance for the infrastructure asset; and

a plurality of pixels likely to present damage of a selected type.

43 . At least one computer-readable storage medium having stored thereon instructions which, when executed, program at least one processor to perform a method comprising acts of:

using a plurality of images captured by a moving camera to generate a point cloud;

identifying, from the point cloud, a plurality of points corresponding to an infrastructure asset;

using the plurality of points to estimate at least one plane;

generating at least one synthetic image of the infrastructure asset, the at least one synthetic image having at least one camera pose perpendicular to the at least one plane; and

using the at least one synthetic image to assess at least one condition of the infrastructure asset.