6D Pose Estimation using an Improved Method based on Point Pair Features

TL;DR

This paper introduces an improved 6D pose estimation method based on Point Pair Features, demonstrating competitive results on multiple datasets and advancing model-based pose estimation techniques.

Contribution

The work presents a variation of the PPF method tailored for the SIXD Challenge datasets, improving accuracy and robustness over previous approaches.

Findings

Achieved an average recall of 0.77 across datasets.

Reported dataset-specific recalls up to 0.97.

Demonstrated the effectiveness of the improved PPF method.

Abstract

The Point Pair Feature (Drost et al. 2010) has been one of the most successful 6D pose estimation method among model-based approaches as an efficient, integrated and compromise alternative to the traditional local and global pipelines. During the last years, several variations of the algorithm have been proposed. Among these extensions, the solution introduced by Hinterstoisser et al. (2016) is a major contribution. This work presents a variation of this PPF method applied to the SIXD Challenge datasets presented at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV 2017. We report an average recall of 0.77 for all datasets and overall recall of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight, rutgers, tejani and doumanoglou datasets, respectively.