# US 2018/0322124 A1 · Automatic registration > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322124 A1 - Document ID: US20180322124A1 - Status: Published application - Kind: A1 - Application number: 16/022,203 - Filed: 2018-06-28 - Published: 2018-11-08 - Earliest claimed: 2013-12-02 - Assignees: Autodesk, Inc. - Applicants: Autodesk, Inc. - Inventors: Oytun Akman; Ronald Poelman; Seth Koterba - Web page: https://patentgrep.com/patent/US20180322124A1 ## CPC - G06V 10/40: Extraction of image or video features - G06F 16/24578: {using ranking} - G06F 16/285: {Clustering or classification} - G06F 16/583: using metadata automatically derived from the content - G06T 17/00: Three-dimensional [3D] modelling for computer graphics - G06T 17/20: Finite element generation, e.g. wire-frame surface description, {tesselation} - G06V 10/757: {Matching configurations of points or features} - G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59) - G06V 20/64: Three-dimensional [3D] objects - G06V 30/2504: {Coarse or fine approaches, e.g. resolution of ambiguities or multiscale approaches} - G06T 2210/56: Particle system, point based geometry or rendering - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A method, system, apparatus, article of manufacture, and computer-readable storage medium provide the ability to merge multiple point cloud scans. A first raw scan file and a second raw scan file (each including multiple points) are imported. The scan files are segmented by extracting segments based on geometry in the scene. The segments are filtered. A set of candidate matching feature pairs are acquired by registering features from one scan to features from another scan. The two raw scan files are merged based on the candidate matching feature pairs ## Claims ### Claim 1 A computer-implemented method for merging multiple point cloud scans, comprising: (a) importing a first raw scan file and a second raw scan file for a scene, wherein each raw scan file comprises multiple points; (b) segmenting the first raw scan file and the second raw scan file by extracting first segments from the first raw scan file and second segments from the second raw scan file, wherein the extracting is based on geometry in the scene; (c) filtering the first segments and the second segments (d) registering one or more of the first features to one or more of the second features to acquire a set of candidate matching feature pairs; (e) merging the first raw scan file with the second raw scan file based on the candidate matching feature pairs, to create a merged scan file. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the filtering: (a) reduces a number of the first segments and second segments; and (b) identifies first features from the reduced number of first segments and second features from the reduced number of second segments. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the registering comprises: coarsely registering one or more of the first features to one or more of the second features; and refining the registering. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the coarsely registering comprises: a rotation estimation; and a translation estimation. ### Claim 5 (depends on 3) The computer-implemented method of claim 3, wherein the refining the registering further comprises coarse refining the registering based on all of the candidate matching feature pairs. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising scoring the candidate matching feature pairs by computing an amount of overlap between the first feature and the second feature of each candidate matching feature pair. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising minimizing errors in the merged scan file using a bundle adjustment operation. ### Claim 8 A computer system for merging multiple point cloud scans comprising: (a) a computer; (b) a point cloud scanner communicatively coupled to the computer, wherein the point cloud scanner obtains a first raw scan file and a second raw scan file for a scene, wherein each raw scan file comprises multiple points; (c) an application executing on the computer that: (1) segments the first raw scan file and the second raw scan file by extracting first segments from the first raw scan file and second segments from the second raw scan file, wherein the extracting is based on geometry in the scene; (2) filters the first segments and the second segments; (3) registers one or more of the first features to one or more of the second features to acquire a set of candidate matching feature pairs; (4) merges the first raw scan file with the second raw scan file based on the candidate matching feature pairs, to create a merged scan file. ### Claim 9 (depends on 8) The computer system of claim 8, wherein the application filters by: (a) reducing a number of the first segments and second segments; and (b) identifying first features from the reduced number of first segments and second features from the reduced number of second segments. ### Claim 10 (depends on 8) The computer system of claim 8, wherein the application registers by: coarsely registering one or more of the first features to one or more of the second features; and refining the registering. ### Claim 11 (depends on 10) The computer system of claim 10, wherein the application coarsely registers using a: a rotation estimation; and a translation estimation. ### Claim 12 (depends on 10) The computer system of claim 10, wherein the application refines the registering by coarse refining the registering based on all of the candidate matching feature pairs. ### Claim 13 (depends on 8) The computer system of claim 8, wherein the application further scores by: computing an amount of overlap between the first feature and the second feature of each candidate matching feature pair. ### Claim 14 (depends on 8) The computer system of claim 8, wherein the application further minimizes errors in the merged scan file using a bundle adjustment operation.