Memory-enhanced Categrory-Level Pose Estimation
Category-level pose estimation jointly estimates the 6D pose: Rotation and translation, and object size for unseen objects with known category labels.
Currently, the SOTA methods in 9D are FS-Net  and DualPoseNet?
And one straightforward idea to improve the performance is to introduce priors into the network.
 and CPS  leverage the point cloud to represent the mean shape of each category. FS-Net adopts the average size of each category.
We, instead, can use a memory module to store typical shapes of each category, similar to point cloud segmentation methods .
The way to establish the memory module:
1 First we train the network to extract features and then utilize the feature to reconstruct observed points, as in FS-Net.
2 Assume the features follow GM distribution, we can use a K-means to build the module, or some other unsupervised learning methods may be doable.
The way to train the network:
Our network structure is similar to FS-Net and ShapePrior?
, thus the training procedures may be similar too.
 Chen, W., Jia, X., Chang, H. J., Duan, J., Shen, L., & Leonardis, A. (2021). FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1581-1590).
 Lin, J., Wei, Z., Li, Z., Xu, S., Jia, K., & Li, Y. (2021). DualPoseNet?
: Category-level 6D Object Pose and Size Estimation using Dual Pose Network with Refined Learning of Pose Consistency. arXiv preprint arXiv:2103.06526.
 Tian, M., Ang, M. H., & Lee, G. H. (2020, August). Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation. In European Conference on Computer Vision (pp. 530-546). Springer, Cham.
 Manhardt, F., Wang, G., Busam, B., Nickel, M., Meier, S., Minciullo, L., ... & Navab, N. (2020). CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning. arXiv preprint arXiv:2003.05848.
 He, T., Gong, D., Tian, Z., & Shen, C. (2020). Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation. arXiv preprint arXiv:2001.01349
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