3D Object Localization Using 2D Estimates for Computer Vision Applications
This addresses object localization for computer vision applications, but appears incremental as it builds on standard techniques like pose estimation and calibration.
The paper tackles 3D object localization by estimating 3D coordinates from multiple 2D images using pose estimation and camera calibration, with validation experiments conducted in MATLAB.
A technique for object localization based on pose estimation and camera calibration is presented. The 3-dimensional (3D) coordinates are estimated by collecting multiple 2-dimensional (2D) images of the object and are utilized for the calibration of the camera. The calibration steps involving a number of parameter calculation including intrinsic and extrinsic parameters for the removal of lens distortion, computation of object's size and camera's position calculation are discussed. A transformation strategy to estimate the 3D pose using the 2D images is presented. The proposed method is implemented on MATLAB and validation experiments are carried out for both pose estimation and camera calibration.