# 6DoF Pose Estimation of Transparent Objects: Dataset and Method

## TL;DR

This paper introduces a new method and dataset for estimating the 3D position and orientation of transparent objects, which is crucial for robotic grasping.

## Contribution

The paper proposes HFF6DoF, a novel hierarchical feature fusion network, and introduces TDoF20, a new dataset for transparent object 6DoF pose estimation.

## Key findings

- HFF6DoF outperforms existing methods on transparent object pose estimation.
- The TDoF20 dataset contains 61,886 RGB-D image pairs for 20 types of transparent objects.
- The method achieves an average ADD of 50.5% on the TDoF20 dataset.

## Abstract

6DoF pose estimation is one of the key technologies for robotic grasping. Due to the lack of texture, most existing 6DoF pose estimation methods perform poorly on transparent objects. In this work, a hierarchical feature fusion network, HFF6DoF, is proposed for 6DoF pose estimation of transparent objects. In HFF6DoF, appearance and geometry features are extracted from RGB-D images with a dual-branch network, and are hierarchically fused for information aggregation. A decoding module is introduced for semantic segmentation and keypoint vector-field prediction. Based on the results of semantic segmentation and keypoint prediction, 6DoF poses of transparent objects are calculated by using Random Sample Consensus (RANSAC) and Least-Squares Fitting. In addition, a new transparent-object 6DoF pose estimation dataset, TDoF20, is constructed, which consists of 61,886 pairs of RGB and depth images covering 20 types of objects. The experimental results show that the proposed HFF6DoF outperforms state-of-the-art approaches on the TDoF20 dataset by a large margin, achieving an average ADD of 50.5%.