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Selective Embedding with Gated Fusion for 6D Object Pose Estimation

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Abstract

Deep learning method for 6D object pose estimation based on RGB image and depth (RGB-D) has been successfully applied to robot grasping. The fusion of RGB and depth is one of the most important difficulties. Previous works on the fusion of these two features are mostly concatenated together without considering the different contributions of the two types of features to pose estimation. We propose a selective embedding with gated fusion structure called SEGate, which can adjust the weights of RGB and depth features adaptively. Furthermore, we aggregate the local features of point clouds according to the distance between them. More specifically, the close point clouds contribute a lot to local features, while the distant point clouds contribute a little. Experiments show that our approach achieves the state-of-art performance in both LineMOD and YCB-Video datasets. Meanwhile, our approach is more robust to the pose estimation of occluded objects.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under grant 61231010.

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Authors and Affiliations

  1. Department of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Shantong Sun, Rongke Liu, Qiuchen Du & Shuqiao Sun

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  1. Shantong Sun
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  2. Rongke Liu
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  4. Shuqiao Sun
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Correspondence to Rongke Liu.

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Appendix

Appendix

The LineMOD dataset contains more than 18,000 real images in 13 video sequences, each of which contains several low-textured objects. It is one of the classic datasets, which is widely used in traditional approaches and recent deep learning methods. Figure 7 shows the qualitative results for all remaining objects on the LineMOD dataset.

Fig. 8
figure 8

Qualitative results of severe occluded objects on the YCB-Video dataset

Full size image
Table 4 Quantitative evaluation of 6D pose (ADD[20] ) on YCB-Video dataset
Full size table

The YCB-Video dataset consists of 21 objects in 92 RGB-D video sequences. It contains a total of 133,827 frames with 3 to 9 objects per frame. The YCB-Video dataset contains many heavily occluded objects. Therefore, it is often used to verify severe occlusion problem in object pose estimation. We compare the pose estimation of objects with more severe occlusion on the YCB-Video dataset in Fig. 8. As shown in Fig. 8, PoseCNN+ICP and DenseFusion perform poorly in estimating the pose of the objects (tomato_soup_can, bowl, cracker_box, wood_block, large_clamp and pudding_box) due to severe occlusion. Our approach is more robust to the pose estimation of heavily occluded objects.

Table 4 lists the results of all 21 objects on the YCB-Video dataset under the ADD metrics. Ours(SEGate) method outperforms DenseFusion by 1.3% on the ADD(< 2 cm) metric and is basically the same on the ADD(AUC) metric. PoseCNN+ICP performs slightly better on the AUC metric, but it is extremely time consuming due to ICP. Note that the MEAN value of ADD(AUC) and the MEAN value of ADD(< 2 cm) are the area under the accuracy curve of all objects in the dataset and the accuracy of pose estimation error which is less than 2 cm of all objects in the dataset respectively, not the average of all 21 objects in Table 4.

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Sun, S., Liu, R., Du, Q. et al. Selective Embedding with Gated Fusion for 6D Object Pose Estimation. Neural Process Lett 51, 2417–2436 (2020). https://doi.org/10.1007/s11063-020-10198-8

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