Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation

Tielin Zhang,Yuxuan Zhao,Yi Zeng,Yang Yang

doi:10.1007/s12559-020-09717-5

Abstract

Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration of the Müller-Lyer illusion in the biological visual system, in this paper, we propose a cognitive template-clustering improved LineMod (CT-LineMod) model. The model uses a 7D cognitive feature vector to replace standard 3D spatial points in the clustering procedure of Patch-LineMod, in which the cognitive distance of different 3D spatial points will be further influenced by the additional 4D information related with direction and magnitude of features in the Müller-Lyer illusion. The 7D vector will be dimensionally reduced into the 3D vector by the gradient-descent method, and then further clustered by K-means to aggregately match templates and automatically eliminate superfluous clusters, which makes the template matching possible on both holistic and part-based scales. The model has been verified on the standard Doumanoglou dataset and demonstrates a state-of-the-art performance, which shows the accuracy and efficiency of the proposed model on cognitive feature distance measurement and template selection on multiple pose estimation under severe occlusion. The powerful feature representation in the biological visual system also includes characteristics of the Müller-Lyer illusion, which, to some extent, will provide guidance towards a biologically plausible algorithm for efficient 6D pose estimation under severe occlusion.

Highlights

The evolutionary procedure of the mammalian brain has resolved the problem of 6D pose estimation by integrating different related brain regions, hundreds of designed neuron types, and functional microcircuits
Algorithms have been proposed [1] in the research area of 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method
The LineMod method is a kind of template matching method which is more efficient for 6D pose estimation compared with other methods based on point pair, Hough forest methods, and deep neural network (DNN)

Summary

Introduction

The evolutionary procedure of the mammalian brain has resolved the problem of 6D pose estimation by integrating different related brain regions, hundreds of designed neuron types, and functional microcircuits. Tielin Zhang and Yang Yang contributed to this article and should be considered as co-first authors. Algorithms have been proposed [1] in the research area of 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. These efforts in machine learning and robotics are still a considerable distance from the performance of the natural biological system. They still face fundamental problems such as sensitivity to illumination changes, noise, blur, and occlusion

Methods

Results

Conclusion