FAST ALGORITHM FOR ONLINE CALIBRATION OF RGB-D CAMERA

• US 20170064287A1
• Filed: 08/23/2016
• Published: 03/02/2017
• Est. Priority Date: 08/24/2015
• Status: Abandoned Application

• Current Assignee: itSeez3D, Inc.
• Original Assignee: itSeez3D, Inc.
• Inventors: Borisov, Alexander
• Application Number:
  US15/244,159
• Publication Number:
  US 20170064287A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 15/04 Texture mapping
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G06T 7/80 Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
• CPC Class Codes: H04N 13/128 Adjusting depth or disparity
• CPC Class Codes: H04N 13/207 using a single 2D image sensor
• CPC Class Codes: H04N 13/211 using temporal multiplexing
• CPC Class Codes: H04N 13/246 Calibration of cameras
• CPC Class Codes: H04N 13/257 Colour aspects

Abstract

The present invention provides a method of producing a 3-dimensional model of a scene.

Claims

1. A method of producing a 3-dimensional model of a scene comprising the steps of:

a) providing a source data having at least one pair of RGB and depth images taken at the same moment in time;

b) calibrating by aligning edges in the depth image with the edges in the RGB image;

c) conducting visual odometry and sparse bundle adjustment from the source data to generate a joint point cloud;

d) conducting a mesh reconstruction from the joint point cloud to produce a surface representation; and

e) generating a texture blending from the surface representation to produce the 3-dimensional model of a scene.

2. The method of claim 1, wherein the step of calibrating comprises:

a) defining depth edge points in each depth image;

b) converting the RGB image to gray scale and identify high intensity gradient areas; and

c) aligning the depth edge points with the high intensity gradient areas.