# 6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features

arXiv: https://arxiv.org/abs/2209.08266 · HTML: https://mlsanity.com/paper/2209.08266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [EPOS: Estimating 6D Pose of Objects with Symmetries](https://mlsanity.com/paper/2004.00605/) (2020)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Going Further with Point Pair Features](https://mlsanity.com/paper/1711.04061/) (2017)
- [ARM3D: Attention-based relation module for indoor 3D object detection](https://mlsanity.com/paper/2202.09715/) (2022)
- [Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition](https://mlsanity.com/paper/1709.02142/) (2017)

## Abstract (arXiv)

The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is proposed to resolve the symmetric ambiguity by calculating edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.