6D Pose Estimation using an Improved Method based on Point Pair Features
This work addresses pose estimation for robotics and computer vision applications, but it is incremental as it builds on existing PPF methods.
The paper tackles 6D pose estimation by proposing a variation of the Point Pair Feature method, achieving an average recall of 0.77 across SIXD Challenge datasets with specific recalls ranging from 0.37 to 0.97.
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.