Validating an SfM map using lidar point clouds

1. A method of validating triangulated points in a digital image, the method comprising,

receiving a motion-generated image corresponding to the digital image;

generating a first depth map, wherein the first depth map comprises first depth information for one or more triangulated points located within the motion-generated image;

receiving a light detection and ranging (lidar) generated point cloud comprising a portion of the one or more triangulated points;

splatting the lidar generated point cloud proximate to the portion of the one or more triangulated points to generate a splatted point cloud;

generating a second depth map for at least the portion of the one or more triangulated points, wherein the second depth map comprises second depth information for the portion of the one or more triangulated points located within the splatted point cloud; and

validating a triangulated point, of the portion of one or more triangulated points, based on comparing the first depth information to the second depth information.

2. The method of claim 1 , wherein the motion-generated image comprises a three-dimensional map generated by Structure from Motion (SfM) imaging.

3. The method of claim 2 , wherein the SfM imaging is generated from a plurality of images comprising one or more common key points.

4. The method of claim 1 , wherein validating the triangulated point further comprises identifying an incorrect triangulated point and removing the incorrect triangulated point from the first depth map.

5. The method of claim 4 , further comprising removing the incorrect triangulated point from the motion-generated image.

6. The method of claim 4 , wherein the identifying the incorrect triangulated point further comprises determining whether the second depth information does not favorably compare, within a predetermined threshold, to the first depth information.

7. The method of claim 1 , wherein validating the triangulated point further comprises determining one or more correct triangulated points, when the second depth information compares favorably, within a predetermined threshold, to the first depth information.

8. The method of claim 7 , wherein the determining one or more correct triangulated points further comprises calculating consensus depth information for a plurality of the one or more triangulated points.

9. The method of claim 7 , further comprising determining a confidence for the one or more correct triangulated points.

10. The method of claim 1 , wherein the generating the first depth map further comprises comparing multiple camera poses for a plurality of camera images.

11. The method of claim 1 , wherein the generating the first depth map further comprises comparing depths from the first depth map of triangulated points against depths from triangulating these points.

12. The method of claim 1 , wherein the splatting comprises rendering opaque surfaces from points within the lidar generated point cloud without connectivity.

13. The method of claim 1 , wherein the splatting comprises determining a neighborhood around key points of the lidar generated point cloud, estimating a surface normal at each of the key points, computing splats with varying radii and rendering the computed splats.

14. A system, comprising:

a memory; and

at least once processor coupled to the memory and configured to:

receive a motion-generated image corresponding to a digital image;

generate a first depth map, wherein the first depth map comprises first depth information for one or more triangulated points located within the motion-generated image;

receive a light detection and ranging (lidar) generated point cloud comprising a portion of the one or more triangulated points;

splat the lidar generated point cloud proximate to the portion of the one or more triangulated points to generate a splatted point cloud;

generate a second depth map for at least the portion of the one or store triangulated points, wherein the second depth map comprises second depth information for the portion of the one or more triangulated points located within the splatted point cloud; and

validate a triangulated point, of the portion of one or more triangulated points, based on comparing the first depth information to the second depth information.

15. The system of claim 14 , wherein the at least one processor is further configured to identify the validated triangulated point as an incorrect triangulated point when the second depth information does not favorably compare, within a predetermined threshold, to the first depth information.

16. The system of claim 14 , wherein the at least one processor is further configured to identify the validated triangulated point as a correct triangulated point when the second depth information favorably compares, within a predetermined threshold, to the first depth information.

17. The system of claim 15 , wherein the at least one processor is further configured to determine consensus depth information for one or more of the correct triangulated points.

18. The system of claim 14 , wherein the at least one processor is further configured to determine a confidence for the correct triangulated point.

19. The system of claim 14 , wherein the wherein the splat comprises:

rendering opaque surfaces from points within the lidar generated point cloud without connectivity.

20. A method of correcting a structure for motion (SfM) map comprising:

receiving a structure for motion-generated map;

generating a first depth map, wherein the first depth map comprises first depth information for one or more triangulated points located within the structure for motion-generated map;

receiving a light detection and ranging (lidar) generated point cloud comprising a portion of the one or more triangulated points;

splatting the lidar generated point cloud proximate to the portion of the one or more triangulated points to generate a splatted point cloud;

generating a second depth map for at least the portion of the one or more triangulated points, wherein the second depth map comprises second depth information for the portion of the one or more triangulated points located within the splatted point cloud;

identifying an incorrect triangulated point, of the portion of one or more triangulated points, based on comparing the first depth information to the second depth information;

and

removing the incorrect triangulated point from the structure for motion-generated map.