vlar-group / dm-nerf Goto Github PK

Why we need the scene center? I don't understand.

In generate_poses_eval function, u list the mani_center:
mani_centers = {'bathroom': [0.779178, 1.05247, 0.380208], 'bedroom': [-1.29552, 1.72703, 0.2946], 'dinning': [-0.633653, 0.295162, 0.279743], 'kitchen': [-2.52579, -0.103821, 1.47165], 'reception': [0.579352, -0.099242, 0.092597], 'restroom': [-0.001277, -2.85079, 0.588084], 'office': [-0.717374, 0.929292, 0.904515], 'study': [-0.519422, -2.16509, 1.07392]}
Can u explain the manipulation matrix generater? Thanks!!

Hello,

Thanks for your great work. Will you release the video of the training dataset?

Thanks

Hi,
thanks for excellent work, how can i do decomposition and reconstruction, is there any avaiiable command

Hi,
I have a self-capture data like kitti style. It has a rgb image, synced point cloud, and 3D bbox label.
How to generate the training data in our data?

Hi,

can you show how to preprocess scannet scenes for training? I tried to run preprocess.py first and then split.py. It gave me train and test split, but when running train_scannet.py it says ins_rgb.hdf5 is missing. How should i get this hdf5 file?

Thank you!

Hi，
thanks for excellent work.
when run Mesh Generation:CUDA_VISIBLE_DEVICES=0 python -u test_dmsr.py --config configs/dmsr/test/meshing.txt.
“ValueError: Surface level must be within volume data range. ”will be reported

Hi！ Thanks for your wonderful work.
In your paper, it says:"Note that, the recent Semantic-NeRF (Zhi et al., 2021a) is also not comparable because it only learns 3D semantic categories, not individual 3D objects."
I’m a little bit confused about why dont you directly use the instance masks to train the semantic-nerf the compare with your work?