Analysis of Optimization Techniques in 6D Pose Estimation Approaches using RGB Images on Multiple Objects with Occlusion

Abstract

6D pose estimation is very important for supporting future smart technologies. The previous methods show optimal performance on RGB-D images or single objects. However, the problem still occurs in RGB images or multiple objects with occlusion. This study focuses on solving the problem using a deep learning approach. One of the key components of deep learning is the optimization process, which we research to determine its effect on solving the problem. The research methodology includes implementing the optimization techniques in the methods, measuring loss value, measuring performance, observing experimental results, analyzing statistical significance, and comparing the performance of optimizers. We implement Adam, RMSprop, Adagrad, Adadelta, and SGD optimizers and analyze their effects on the EfficientPose and DPOD methods. We use the LineMod-Occluded dataset to measure the performance of the methods using the ADD metric. According to the experiment, the loss value is low and stable in the experimental scenarios with a number of epochs between 200 and 500. The performance is relatively high in those scenarios, where Adadelta's performance outperforms other optimizers on both methods. Based on the analysis of variance, the effect of optimizers on the performance of the methods is low, but the slight performance increase is significant in this case.