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License: Apache License 2.0
Repository of 360BEV
License: Apache License 2.0
Dear Authors:
When I downloaded the 360-FV dataset, I had found it didn't include label file and it had 42 numbers in .png. Besides, The dataset includes "self.df = pd.read_csv("eigen13_mapping_from_mpcat40.csv")", it is hard to find the .csv for me. Would you mind sending it to me?
Bests.
Your work is very good, but I don't know how to organize the data you provide in Google Drive into a suitable format and test it
Hello,
Firstly, I'd like to thank you for sharing this interesting codebase. I have been reviewing the BEV360_segnext_s2d3d.py script and have a couple of questions I hope you could clarify for me:
Could you please explain how the masks_inliers variable is derived in the code? I'd like to understand the logic behind it.
Regarding the variable m, it seems to be derived in a rather unusual way. The threshold_index_m is set as the maximum value of proj_index. As a result, the boolean mask m will mark all elements in proj_index that are smaller than this maximum value as True. The only exception, where m would be False, is when an element in proj_index equals threshold_index_m. Could you please elaborate on the reasoning behind this approach?
I appreciate your time and assistance in advance. Looking forward to your response.
Best regards
Dear authors,
Hello, I greatly admire your impactful and innovative work: 360BEV:Panoramic Semantic Mapping for Indoor Bird’s-Eye View
When I read your excellent work, I saw:
"The original Matterport3D [5] was collected via narrow-FoV cameras. As shown in Fig. 3, we convert the 18 narrow-view images and annotations into the 360° format by using rotation-translation matrices."
Could you please provide the process code of acquiring Matterport3D panoramic segmentation image?
Thanks!
Dear authors,
thanks for your excellent work! I have some questions about the 360FV-Matterport dataset. It would be great if you can help me out.
Thank you in advance for your help.
Best,
Junwei
In the encoder section of your code, I noticed that the index doesn't appear to be utilized. Instead, you seem to recompute the corresponding panorama index based on the mask height. Given that you've already calculated smnet_training_data_maxHIndices, why isn't this index used directly instead of recalculating it?
I noticed that the project makes use of a checkpoint file named mit_b2.pth. Could you please provide some information about its origin? Specifically:
Where was this checkpoint file downloaded from?
Is it a pre-trained model that does not require further training to be used?
Thank you for your time and effort on this project. Looking forward to your response.
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