Research On Pose Estimation Of Three-Dimensional Models Based On The Obiect Coordinate Representation

As a new audio-visual technology centered on computers,augmented reality technology renders a digital environment that is highly similar to the real environment in terms of vision,hearing,and touch within a certain range,and provides the possibility for users to interact with 3D objects in the digital environment.At the same time,in the field of surgical robots,the accurate physical distance and orientation of objects in the environment help the robot complete path planning and navigation tasks.The 6D pose estimation of 3D objects has a wide range of applications in the above-mentioned scenes.The number and types of objects in a scene image are not available in advance which needs it to recognize them first and then estimate the 6D poses(including 3D rotation and 3D translation).However,due to the diversity of objects in the real world and the projection appearance on the 2D plane is affected by a variety of conditions includes the illumination,the complexity of the background of the scene,and the occlusion between objects,the 6D pose estimation problem has challenges.This paper extends the research of the 6D pose estimation of 3D models into two aspects:(1)In the study of 6D pose estimation of 3D objects for transcranial magnetic stimulation surgery,considering that the marker-based camera 6D pose estimation method has the advantages of easy detection,high speed and high accuracy,this paper adopts a marker-based method to obtain the camera pose.Also,this paper tests the involvement of image enhancement algorithms and Super-Resolution technologies to advocate the detection rate and minimize the average distance metric.(2)In the research of Category-level object pose estimation,due to the advanteges of the object coordinate representationthat can minimize the number of candidate hypothesizes and efficiently deal with the occlusion and complex background situation,this paper adopts the object coordinate representation as a middle representation to establish a complete set of correspondence.Also,considering that the Preemptive RANSAC algorithmcan avoid excessive scoring to hypothesis which is pollutedby outliers or distorted by noise,therefore,this paper adopts the Preemptive RANSAC algorithm to remove outliers of hypothesis.Also,this paper proposes a procedure named pose correction process to reorganize candidates for the next iteration by carefully select the inlier set.Experiments demonstrates:Involving the Super-Resolution technology can effectively increase the detection rate and decrease the average distance metric and the method proposed can simulate the precise registration among 3D models with the actual targets which can promote the application of the augmented reality during the TMS surgery;By comparing various pose estimation schemes,the experimental results show that the category-level pose estimation model proposed in this paper can improve the accuracy of pose estimation.