Systems and methods for assessing property damage
View Patent ↗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.
1. A computer implemented method for estimating damage to a property, the method comprising:
scanning a property with a three dimensional (3D) scanner to generate a 3D point cloud of the scanned property;
storing the 3D point cloud to a memory device;
causing one or more processors to access the 3D point cloud stored at the memory device and to implement a damage assessment module for:
(i) comparing a first segment of the generated 3D point cloud to a model point cloud, the first segment corresponding to a first object of a plurality of objects detectable at a surface of the property;
(ii) identifying based on the comparison of the first segment to the model point cloud, a set of outlier points from the first segment; and
(iii) associating the identified set of outlier points with a damage status;
generating, by said one or more processors and based at least in part on the damage status associated with the set of outlier points, a report of estimated damage to the property; and
displaying, at a user interface device, the generated report of estimated damage to the scanned property.
2. The method of claim 1 , wherein:
the property is a building; and
the object is one of a plurality of objects on the 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 first segment corresponds to a point cluster, identified from the 3D point cloud, forming a first planar region.
5. The method of claim 4 , further comprising identifying, by said one or more processors, the first segment of the 3D point cloud, wherein identifying the first segment of the 3D point cloud includes utilizing region growing segmentation to isolate, from the 3D point cloud, the point cluster forming the first planar region.
6. The method of claim 5 , wherein utilizing region growing segmentation to isolate, from the 3D point cloud, the point cluster forming the first planar region, comprises:
identifying, by said one or more processors, a set of candidate points, the set of candidate points including one or more first points from the 3D point cloud;
determining, by said one or more processors, a first measurement of a surface shape associated with the set of candidate points;
identifying, by said one or more processors, one or more second points from the 3D point cloud;
adding, by said one or more processors, the one or more second points to the set of candidate points;
removing, by said one or more processors, the one or more second points from the set of candidate points when a second measurement of a surface shape associated with the set of candidate points exceeds a threshold shape-measurement; and
storing the set of candidate points to a memory as the first segment.
7. The method of claim 6 , wherein the first measurement, the second measurement, and the threshold shape-measurement each relate to at least one of: (i) curvature, or (ii) smoothness.
8. The method of claim 1 , wherein the model point cloud includes points identified from each of a plurality of segments of point clouds, the plurality of segments including the first segment.
9. The method of claim 1 , wherein the model point cloud represents an average of point clusters corresponding to other objects detectable at a surface of the property.
10. The method of claim 1 , wherein identifying, by one or more processors and based on the comparison of the first segment to the model point cloud, the set of outlier points from the first segment includes:
determining, for each point of the first segment, a deviation measurement representing a deviation relative to a corresponding point in the model point cloud.
11. The method of claim 1 , wherein identifying, by said one or more processors, the set of outlier points from the first segment comprises utilizing Euclidean clustering to identify the set of outlier points from the first segment.
12. The method of claim 1 , wherein associating, by one or more processors, the identified set of outlier points with the damage status comprises:
determining, by said one or more processors, whether one or more of the points in the set of outlier points form a cluster of outlier points proximate to each other such that the cluster of outlier points represent an identifiable area of potential damage to the object.
13. The method of claim 1 , wherein the damage status represents at least one of: (i) a false positive associated with the identified set of outlier points, the false positive indicating that the set of outlier points represents an undamaged area of the object; or (ii) an extent of damage associated with the identified set of outlier points.
14. The method of claim 1 , further including mapping, by said one or more processors, the set of outlier points associated with the damage status to the 3D point cloud representing the property so that one or more areas of damage to the property may be estimated from the 3D point cloud representing the property.