Multiple Depth Cameras Calibration and Fusion Algorithm for Object Reconstruction

Abstract

This thesis presents an efficient calibration flow for multi-depth-camera systems together with an image fusion process for 3-dimensional (3-D) object reconstruction With the proposed three-step calibration scheme the intrinsic and extrinsic parameters of multiple depth cameras can be accurately estimated Then the images captured by the system can be fused to derive a seamless 3-D model using the presented object reconstruction process Inspired by the traditional calibration scheme using known patterns such as the checkerboard we adopted a given 3D model as the reference geometry to refine camera parameters First by segmenting the images according to the reference geometry and then applying the iterative closet point (ICP) algorithm we can obtain the transformation and matching relationship between the measured point set and the actual values in reference model The intrinsic and extrinsic parameters of the cameras can be further optimized by applying the coordinate descent algorithm Moreover since the density of the measured points decreases quadratically with the distance along the camera axis the closer points should have a more pronounced impact on the optimization process than the farther ones Thus the axial effect is considered by introducing clustering refinement during calibration to avoid the over-fitting problem To relax the random error of depth cameras this thesis adopts a two-step reconstruction method to fit the measured surface based on two different implicit functions for eliminating the high-frequency noise and outliers Furthermore we observed that the depth camera distortion is related to the characteristics of the object itself and experimental results also reveal that the reconstructed results can be further improved as the topology of the reference geometry is approaching to that of the targeted objects The proposed schemes can be adapted to different application requirements by altering the reference geometry and achieve a better calibration and reconstruction results than the conventional approaches

Date of Award	2019
Original language	English
Supervisor	Ming-Der Shieh (Supervisor)