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

arXiv: https://arxiv.org/abs/1802.08516 · HTML: https://mlsanity.com/paper/1802.08516/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 11 of 11)

- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation](https://mlsanity.com/paper/2303.15743/) (2023)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement](https://mlsanity.com/paper/2404.11139/) (2024)
- [CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation](https://mlsanity.com/paper/2211.13398/) (2022)
- [Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation](https://mlsanity.com/paper/2503.13053/) (2025)
- [Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking](https://mlsanity.com/paper/2106.08045/) (2021)
- [Open Challenges for Monocular Single-shot 6D Object Pose Estimation](https://mlsanity.com/paper/2302.11827/) (2023)
- [Instance by Instance: An Iterative Framework for Multi-instance 3D Registration](https://mlsanity.com/paper/2402.04195/) (2024)
- [6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping](https://mlsanity.com/paper/2111.06276/) (2021)
- [Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings](https://mlsanity.com/paper/2310.14914/) (2023)

## Abstract (arXiv)

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.