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In the article, when generating the label, three occupancy states were generated: free, occupied, and unobserved. However, the network made a binary classification to determine whether the voxel was empty (occupied or not?).
So what is the significance of adding unobserved states (for camera-based tasks)?
The text was updated successfully, but these errors were encountered:
Thanks for this excellent work.
In the article, when generating the label, three occupancy states were generated: free, occupied, and unobserved. However, the network made a binary classification to determine whether the voxel was empty (occupied or not?).
So what is the significance of adding unobserved states (for camera-based tasks)?
The text was updated successfully, but these errors were encountered: