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I followed your advice in Issue #13 to infer the semantic labels associated with the bounding boxes, but have only succeeded in inferring 77% of them from the instance.png and semantic.png images of each scene (for 438311 bounding boxes, only 337345 could be inferred, that is more exactly 76.9647579002%).
This missing quarter of information is quite big, and seems as I feared in my message in Issue #13, to be due to object occlusion in the images. Therefore, it seems that it is impossible for end users to infer the missing data. Would you please consider filling in the gap =) ?
Wish you a nice day !
The text was updated successfully, but these errors were encountered:
@supriya-gdptl I just uploaded the code I wrote to infer the semantic labels from the images for the bounding boxes on a fork of this repository: https://github.com/Xia0ben/Structured3D. I do not have the time to cleanup/document the code right now, so it would be nice to do a pull-request directly to @bertjiazheng 's main repo if you were to use it and clean it up a bit. I mainly reused existing code written by @bertjiazheng , there is not much more to it but it would indeed be helpful if a proper script were added to the main repo. ;-)
Hello,
I followed your advice in Issue #13 to infer the semantic labels associated with the bounding boxes, but have only succeeded in inferring 77% of them from the instance.png and semantic.png images of each scene (for 438311 bounding boxes, only 337345 could be inferred, that is more exactly 76.9647579002%).
This missing quarter of information is quite big, and seems as I feared in my message in Issue #13, to be due to object occlusion in the images. Therefore, it seems that it is impossible for end users to infer the missing data. Would you please consider filling in the gap =) ?
Wish you a nice day !
The text was updated successfully, but these errors were encountered: