nightmare-n / unipad Goto Github PK

Thank you for your work. I would like to ask how sampling 512 rays per image view achieves such a high rendering resolution in the final render image(figure3 in your paper). For a high-definition image of 1600 900, 512 seems like a very small number.

Hi, thank you for sharing this excellent work! When i try to install this project using python setup.py develop --user, i met this compilation error

I wonder how can i solve this errors, hope for your reply!!

Thank you for releasing this amazing work! I just had a couple questions on some of the camera-only outdoor details @Nightmare-n

1. Are the same 6 images used for generating the 3D voxel grid and rendering (as is mentioned to be done for ScanNet in PonderV2)?
2. Were the used ConvNeXt(V1?) backbones trained from scratch or with IN1k?
3. Was any data augmentation used for the 2D training stage, besides regular MAE masking?
4. When using the proposed depth-aware sampling, are the same 512 rays, sampled from the pixels with available LiDAR points, used for both color & depth rendering?
5. In Table 8g, there appear to be trainable weights associated with the view transformation stage. Does the view transformation generally follow UVTR(?) with multi-scale sampling and depth weighting? Or perhaps single-scale?
6. In PonderV2, supplementary is mentioned. Would it be possible for this to be made public?
7. What (total) batch size was used?

Apologies for the list of questions, but I'm really interested in the work. Again, thank you so much in advance!