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Article summary:
1. PoseCNN is a new Convolutional Neural Network for 6D object pose estimation.
2. A novel loss function is introduced to enable PoseCNN to handle symmetric objects.
3. The YCB-Video dataset is contributed, providing accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames.
Article analysis:
The article appears to be reliable and trustworthy as it provides detailed information about the PoseCNN algorithm and its performance on the YCB-Video dataset and OccludedLINEMOD dataset. The authors also provide their code and dataset for further research, which adds to the credibility of the article. However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to 6D object pose estimation, nor do they discuss any possible risks associated with using their approach. Additionally, they do not present both sides of the argument equally; instead they focus solely on promoting their own approach without considering other methods or solutions that may be available.