IP Library › Granted Patent US 12,450,664
Granted Patent B2
US 12,450,664 · App. 18/110,527 · Granted Oct 21, 2025

Assessing property damage using a 3D point cloud of a scanned property

Inventors: Bryan Allen Plummer (Urbana, IL); Drew Cross (Geneseo, IL); Nathan L. Tofte (Downs, IL)
Assignee: Roofr Inc.
G06Q40/08G06F30/20G06Q20/10G06Q30/0278G06Q40/00G06T7/00G06T7/0004G06T7/187G06T15/08G06T17/00H04N13/204G06T2210/56G06T2219/012
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Quick Facts
Patent No.
US 12,450,664
App. No.
18/110,527
Filed
Feb 16, 2023
Granted
Oct 21, 2025
Kind
B2
Art Unit
3693
USPC
705/4
Abstract

A damage assessment module operating on a computer system automatically evaluates a property, estimating damage to the property by analyzing a point cloud of a property. The damage assessment module identifies individual point clusters or segments from the point cloud and detects potentially damaged areas of the property surface by identifying outlier points in the point clusters. The damage assessment module may be used to determine the financial cost of the damage and/or determine whether the property should be replaced or repaired. In addition to eliminating the need for an estimator to visit the property in person, the damage assessment module improves the consistency and accuracy associated with estimating damage to a property.

Claims (42)

1. A computer implemented method for estimating damage to objects on a property, the method comprising:

causing one or more processors to access a memory storing: (a) a 3D point cloud representing a property scanned by a 3D scanner and (b) a damage assessment module; and

implementing, by the one or more processors, the damage assessment module, including:

(i) identifying a segment of the 3 D point cloud that represents an object on the property;

(ii) performing an analysis of the segment and a model point cloud representing a model object, wherein the analysis includes reducing effects of noise in the 3D point cloud by identifying false positives representing clusters that are misidentified as damaged regions;

(iii) detecting, based on the analysis of the segment and the model point cloud, that the object represented by the segment of the 3D point cloud is damaged;

(iv) generating a damage status indicating a degree to which the object is damaged; and

(v) automatically generating, based at least in part on the damage status, a report of estimated damage to the object.

2. The method of claim 1 , wherein:

the property is a building; and

the object is one of a plurality of objects on a surface of the property, the plurality of objects selected from a group consisting of: (i) a plurality of tiles; (ii) a plurality of shingles; (iii) a plurality of bricks; (iv) a plurality of siding panels; and (v) a plurality of wood planks.

3. The method of claim 1 , wherein:

the property is a vehicle; and

the object is one of a plurality of body panels of the vehicle.

4. The method of claim 1 , wherein:

the segment corresponds to a point cluster, identified from the 3D point cloud, forming a planar region.

5. The method of claim 1 , wherein identifying the segment of the 3D point cloud includes utilizing region growing segmentation to identify the segment.

6. The method of claim 1 , wherein identifying the segment of the 3D point cloud includes utilizing planar segmentation to identify the segment.

7. The method of claim 1 , wherein identifying the segment of the 3D point cloud includes utilizing difference of normals segmentation to identify the segment.

8. The method of claim 1 , wherein the model point cloud represents a model object having experienced an expected level of wear and tear.

9. The method of claim 1 , further comprising: generating the model point cloud representing the model object based on a plurality of point clouds representing a plurality of scanned objects, such that the model object represents an average of the plurality of scanned objects.

10. The method of claim 1 , wherein generating the damage status includes classifying the damage to the damaged object according to one or more classifications.

11. The method of claim 10 , wherein the one or more classifications includes a classification indicating whether or not the damage is hail damage.

12. The method of claim 8 , wherein the property is a building, and the one or more classifications includes a classification indicating whether or not the damage is wind damage.

13. The method of claim 1 , wherein the generated report of estimated damage to the object is displayed in the damage assessment module at a user interface device.

14. A system for estimating damage to objects on a property, the system comprising:

one or more 3D scanners for scanning a property to generate a 3D point cloud representing the property;

one or more memories for storing the 3D point cloud representing the property; and

one or more processors coupled to the one or more memories;

wherein the one or more memories include computer readable instructions that, when executed, cause the one or more processors to:

(i) identify a segment of the 3D point cloud that represents an object on the property;

(ii) perform an analysis of the segment and a model point cloud representing a model object, wherein the analysis includes reducing effects of noise in the 3D point cloud by identifying false positives representing clusters that are misidentified as damaged regions;

(iii) detect, based on the analysis of the segment and the model point cloud, that the object represented by the segment of the 3D point cloud is damaged;

(iv) generate a damage status indicating a degree to which the damaged object is damaged; and

(v) automatically generate, based at least in part on the damage status, a report of estimated damage to the property.

15. The system of claim 14 , wherein:

the segment corresponds to a point cluster, identified from the 3D point cloud, forming a planar region.

16. The system of claim 14 , wherein the instructions to identify the segment of the 3D point cloud comprise instructions that, when executed, cause the one or more processors to utilize region growing segmentation to identify the segment.

17. The system of claim 14 , wherein the instructions to identify the segment of the 3D point cloud comprise instructions that, when executed, cause the one or more processors to utilize planar segmentation to identify the segment.

18. The system of claim 14 wherein the instructions to identify the segment of the 3D point cloud comprise instructions that, when executed, cause the one or more processors to utilize difference of normals segmentation to identify the segment.

19. The system of claim 14 , wherein the model point cloud represents a model object having experienced an expected level of wear and tear.

20. The system of claim 14 , further comprising instructions that, when executed, cause the one or more processors to: generate the model point cloud representing the model object based on a plurality of point clouds representing a plurality of scanned objects, such that the model object represents an average of the plurality of scanned objects.

Assignments (3)
SECURITY INTEREST Recorded Oct 20, 2025
From: ROOFR INC.
To: STIFEL BANK
Reel/Frame 072598/0354 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 21, 2025
From: STATE FARM MUTUAL AUTOMOBILE INSURANCE CO.
To: ROOFR INC.
Reel/Frame 072083/0039 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 3, 2023
From: PLUMMER, BRYAN ALLEN; CROSS, DREW; TOFTE, NATHAN L.
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 063204/0052 →
Continuity (6)
Continuation 17083819 · Oct 29, 2020
Continuation 15456016 · Mar 10, 2017
Continuation 14269920 · May 5, 2014
Continuation 14047844 · Oct 7, 2013
Provisional Application 61799452 · Mar 15, 2013
Related Publication 20230196475A1 · Jun 22, 2023
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