seokjulee / insta-dm Goto Github PK

Dear authors

I was wondering how you obtain the backward flow of the first frame and the forward flow of the last frame in a scene.
Ex: there are 000.jpg, 001.jpg, 002.jpg in a scene
000.jpg, 001.jpg => forward_000.flo
(no -001.jpg to compute backward_000.flo)
001.jpg, 002.jpg => forward_001.flo
001.jpg, 000.jpg => backward_001.flo
002.jpg, 001.jpg => backward_002.flo
(no 003.jpg to compute backward_002.flo)

Thank you very much.

Best,
Bolian

How are the pre-training weights of each sub-network obtained? How are the sub-networks supervised learning?

Is it possible to use this method in realtime on a video stram from a webcam?

I've read the paper and it's amazing work when would plan to make the repo publicly available? Thank you!

Dear authors:
Thank you for your great work in this paper ! If I want to reproduce it on my custom dataset, how should I preprocess my image data to obtain the instance segmentation results.

Hi @SeokjuLee

I noticed that the Cityscape-VIS dataset does not include the GT depth, could you share a link to download the GT data as used in your paper? Thank you!

Hi, Seokju!
I get only 1636 iterms, but the "train.txt" and "val.txt" indicate there should be 4883+602 = 5485.
Did I make a mistake?

Hi @SeokjuLee :

Thank you for sharing this amazing work, the visualization in this code helps me a lot in understanding the logic. However, I have a confusion, the line below:

There are two instance masks in r2t_inst, are you supposed to visualize one of them like r2t_inst[:, :, 0] or do you have other implementations to visualize both of them in once?

Thank you!

By running sh scripts/run_demo.sh, I get the following error:

/torch/lib/python3.6/site-packages/torch_sparse/_version_cuda.so: undefined symbol: ZN3c106detail12infer_schema20make_function_schemaENS_8ArrayRefINS1_11ArgumentDefEEES4

i install exactly
pytorch=1.7.0 torchvision torchaudio cudatoolkit=11.0 -c pytorch
and
pip3 install torch-scatter torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html

Hi, thank you for sharing this amazing work, I just have a little bit confusion. Does the AbsRel error of 0.112 on kitti eigen split with or without imagenet pretraining? If not, do you have results with pretraining?

Thank you

I found that the motion of objects in the code is 3-Dof, but the paper claims that it estimates 6-Dof motion of objects. So where is the 6-Dof motion estimation of objects? The prediction of ego pose is 6-Dof, but the prediction of object pose is 3-Dof