Scale error correction in a multi-dimensional model

• US 9,830,742 B2
• Filed: 10/24/2016
• Issued: 11/28/2017
• Est. Priority Date: 01/31/2014
• Status: Active Grant

• Current Assignee: Hover Inc. (Tucows Incorporated)
• Original Assignee: Hover Inc. (Tucows Incorporated)
• Inventors: Bhatawadekar, Vineet, Sun, Shaohui, Pavlidis, Ioannis, Altman, Adam J.
• Primary Examiner(s): Liu, Zhengxi
• Assistant Examiner(s): Cofino, Jonathan M
• Application Number:
  US15/332,481
• Publication Number:
  US 20170061684A1
• Time in Patent Office: 400 Days
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 19/20 Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/30184 Infrastructure
• CPC Class Codes: G06T 2210/04 Architectural design, interior design
• CPC Class Codes: G06T 2219/2016 Rotation, translation, scaling
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/13 Edge detection

Abstract

A system and method is provided for identifying error and rescaling and constructing or reconstructing a multi-dimensional (e.g., 3D building) model using known architectural dimensions. The system identifies architectural elements that have known architectural standard dimensions. Dimensional measurements of architectural elements in the multi-dimensional model (poorly scaled) are compared with known architectural standard dimensions to rescale and construct/reconstruct an accurate multi-dimensional building model.

Claims

1. A computing device 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;

retrieve, from a multi-dimensional model database, a multi-dimensional model;

identify an architectural element located on one or more surfaces of the multi-dimensional model;

define a plurality of boundaries for the architectural element;

determine measurements between two or more of the plurality of boundaries of the architectural element;

compare the determined measurements to a plurality of known similar architectural element'"'"'s standard dimensions, within an error threshold;

select, based on the comparing, actual dimensions of a known similar architectural element that is dimensionally closest to the architectural element;

determine an error in scaling of the multi-dimensional model based on comparing the determined measurements to respective dimensions on the known similar architectural element;

perform a statistical analysis for a determined error in scaling for a plurality of architectural elements and selecting a minimum statistical occurrence of the error

in scaling, wherein the statistical analysis predicts, based on a comparison of the determined error in scaling, which architectural element of at least two or more architectural elements of the plurality of architectural elements would produce a most accurate rescaled multi-dimensional model; and

rescale the multi-dimensional model according to the minimum statistical occurrence of the error.

2. The computing device of claim 1, wherein the multi-dimensional model includes any of:

a two-dimensional (2D) building, a three-dimensional (3D) building or a building interior.

3. The computing device of claim 1 further comprises reconstructing the resealed multi-dimensional model.

4. The computing device of claim 1, wherein dimensions are determined automatically or manually.

5. The computing device of claim 1, wherein dimensions are determined by a plurality of geo-referenced pixels.

6. The computing device of claim 1, wherein the retrieved multi-dimensional model includes imagery rectified and correlated to vertices of geometric planes or polygons of the multi-dimensional model.

7. The computing device of claim 1, wherein the statistical analysis is performed by any of:

a weighted decision engine or a learned statistical analysis system.

8. The computing device of claim 1, wherein the architectural element includes any of:

siding row(s), door(s), brick(s), or window(s), each having known architectural standard dimensions.

9. The computing device of claim 1, wherein the actual dimensions include any of:

width, height, width and height, area, or a ratio of width-to-height or height-to-width.