Recovering 6D Object Pose: A Review and Multi-modal Analysis

TL;DR

This paper reviews 6D object pose estimation methods, emphasizing multi-modal analysis of RGB-D data, and discusses challenges, current techniques, and future directions for improving robotic autonomy.

Contribution

It provides a comprehensive review of 6D pose estimation techniques, compares RGB and RGB-D methods, and highlights key challenges and future research directions.

Findings

Accurate pose estimation on textured objects with cluttered backgrounds.

Occlusion and distractors significantly hinder detection accuracy.

Deep learning approaches, especially CNNs, are increasingly used for 6D pose estimation.

Abstract

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem.