Real-time processing of captured building imagery
A process for receiving, from a computing device, a series of captured building images and processing, in real-time, each building image in the series of captured building images to determine if the building image meets a minimum threshold for quality. The process continues, for each building image meeting the minimum threshold for quality, by returning a first quality indication to the computing device and for each building image not meeting the minimum threshold for quality, by returning a second quality indication to the computing device. When a complete set of quality building images has been received, the process continues by creating at least a partial multi-dimensional building model based at least partially on the complete set of quality building images.
1. A method of processing building imagery, the method comprises:
receiving, from a computing device, a series of captured building images;
processing, in real-time, each building image in the series of captured building images to determine if the building image meets a minimum threshold for quality;
for each building image meeting the minimum threshold for quality, storing this building image in computer memory and returning a first quality indication to the computing device;
for each building image not meeting the minimum threshold for quality, discarding this building image and returning a second quality indication to the computing device;
determining when a complete set of quality building images has been received, wherein the complete set of quality building images includes a minimum grouping of images to construct at least a partial multi-dimensional building model representing the series of captured building images, where the at least a partial multi-dimensional building model includes surfaces shown in the minimum grouping of images; and
creating at least a partial multi-dimensional building model based at least partially on the complete set of quality building images.
2. The method of claim 1 , wherein the processing includes comparing each captured building image in the series of captured building images to a database of previously stored quality images.
3. The method of claim 2 , wherein the determining if the captured building image meets a minimum threshold for quality is based on any of: a trained neural network-based quality image assessment method, a deep learning-based quality image identifying method, an artificial intelligence (AI) based quality image assessment method, or a machine learning-based quality image assessment method.
4. The method of claim 1 , wherein a minimum threshold for quality includes any of: predetermined acceptable camera-based image parameters, applicability in constructing a multi-dimensional model, or capturing instances of specific architectural features to be included in model construction.
5. The method of claim 1 , wherein the minimum threshold for quality includes capturing at least a first corner and one or more sides of a corresponding building.
6. The method of claim 1 , wherein the minimum threshold for quality includes capturing all instances of at least a first architectural feature for a corresponding building.
7. The method of claim 1 , wherein the minimum threshold for quality includes capturing a specific perspective of a corresponding building.
8. The method of claim 1 , wherein the first quality indication is one or more of: good quality or best available quality.
9. The method of claim 1 , wherein the second quality indication is one or more of: bad quality or unacceptable quality.
10. The method of claim 1 , wherein the first or second quality indication is provided as a grade between a low and high number ranking from bad to good.
11. A multi-dimensional modeling server comprises:
an interface;
a local memory; and
a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:
receive, from a computing device, a series of captured building images;
process, in real-time, each captured building image in the series of captured building images to determine if the captured building image meets a minimum threshold for quality;
for each of the captured building images meeting the minimum threshold for quality, store these captured building images in computer memory and return a first quality indication to the computing device;
for each of the captured building images not meeting the minimum threshold for quality, discard these captured building images and return a second quality indication to the computing device;
determine when a complete set of quality building images has been received, wherein the complete set of quality building images includes a minimum grouping of images to construct at least a partial multi-dimensional building model representing the series of captured building images, where the at least a partial multi-dimensional building model includes surfaces shown in the minimum grouping of images; and
create at least a partial multi-dimensional building model based at least partially on the complete set of quality building images.
12. The multi-dimensional modeling server of claim 11 further configured to compare each building image in the series of captured building images to a database of previously stored quality images.
13. The multi-dimensional modeling server of claim 12 , wherein the determine if the captured building image meets a minimum threshold for quality is performed by any of a non-transitory computer readable medium encoded with any of: a trained neural network, deep learning, artificial intelligence, or machine learning.
14. The multi-dimensional modeling server of claim 11 , wherein the minimum threshold for quality includes at least a first corner and one or more sides of a corresponding building.
15. The multi-dimensional modeling server of claim 11 , wherein the minimum threshold for quality includes a specific perspective of a corresponding building.
16. The multi-dimensional modeling server of claim 11 , wherein the minimum threshold for quality includes a building image with a boundary inclusion of a corresponding building.
17. The multi-dimensional modeling server of claim 11 , wherein the series of captured building images are received from a mobile camera.
18. The multi-dimensional modeling server of claim 11 , where the multi-dimensional modeling server is configured to run directly on a mobile camera or image capture device.
19. An image processing system comprises:
an interface;
a local memory;
a machine learning module; and
a processing module operably coupled to the interface, the local memory and the machine learning module, wherein the processing module is configured to:
receive, from a computing device, a series of captured building images;
receive in real-time, from the machine learning module, an indication for each captured building image in the series of captured building images meeting a minimum threshold for quality;
for each of the captured building images meeting the minimum threshold for quality, store these captured building images in computer memory and return a first quality indication to the computing device;
for each of the captured building images not meeting the minimum threshold for quality, discard these captured building images and return a second quality indication to the computing device;
determine when a complete set of quality building images has been received, wherein the complete set of quality building images includes a minimum grouping of images to construct at least a partial multi-dimensional building model representing the series of captured building images, where the at least a partial multi-dimensional building model includes surfaces shown in the minimum grouping of images; and
create at least a partial multi-dimensional building model based at least partially on the complete set of quality building images.
20. The image processing system of claim 19 , wherein the image processing system is configured to run directly on a mobile camera or image capture device.