cogtoolslab / physics-benchmarking-neurips2021

Repo for "Physion: Evaluating Physical Prediction from Vision in Humans and Machines", presented at NeurIPS 2021 (Datasets & Benchmarks track)
MIT License
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Use of depth image #55

Open jsw2000 opened 1 year ago

jsw2000 commented 1 year ago

Hi,

I have followed some graph-based networks on Physion. It is an excellent work, and I believe there will be lots of reserachers following this work. These days, I have some questions on the use of depth data:

  1. Let me use 'f' to represent one of your h5df files. Does f['frames'][‘0000']['images']['_depth'] mean depth image? It is a (512,512,3) image. And it is only a little different from rgb image, so it may not be. Could you tell me where could I find depth image in your hdf5 file?

2.['_depth'],['_normal'],['_flow'], these keys only appear in test hdf5 files, and I can't find them in training files. How to use these features in training phase?

Thank you very much if you would like to help!

felixbinder commented 1 year ago

Hi, thanks a lot for your question! I've tried reading out the depth data from a HDF5 from that dataset, and I can confirm that it indeed doesn't contain depth data, but rather the RGB data. That doesn't seem right—I'll follow up with some other folks on the project (@danielbear).

Re the training files: IIRC we didn't generate the depth data for the training data to keep the size manageable (the HDF5s can be quite large). If you need access to those fields, you could consider regenerating the data with the right flags set. The code to generate the dataset can be found here: https://github.com/neuroailab/tdw_physics/tree/Neurips2021

jsw2000 commented 1 year ago

Hi, thanks a lot for your question! I've tried reading out the depth data from a HDF5 from that dataset, and I can confirm that it indeed doesn't contain depth data, but rather the RGB data. That doesn't seem right—I'll follow up with some other folks on the project (@danielbear).

Re the training files: IIRC we didn't generate the depth data for the training data to keep the size manageable (the HDF5s can be quite large). If you need access to those fields, you could consider regenerating the data with the right flags set. The code to generate the dataset can be found here: https://github.com/neuroailab/tdw_physics/tree/Neurips2021

Thanks for your reply!

danielbear commented 1 year ago

Hi there,

Thanks for your interest in our work and the Physion dataset! Sorry about the issue with the _depth field. I actually remember that there was a bug in the ThreeDWorld back-end that generated RGB instead of depth when using MacOS. It's possible that's fixed, and if not submitting a PR to https://github.com/threedworld-mit/tdw might get it resolved. Either way, your best bet is to try regenerating the data you need depth for; Linux or Windows might be better if you can.

Same goes for the training data. As Felix mentioned, we only generated RGB for the training data to keep the file sizes smaller. But you can actually generate them at whatever resolution you need and with whichever "passes" you want (e.g. flow, depth, segments.)

Please let us know if the instructions for generating data, which Felix linked to, are unclear or don't work for you!

Dan

On Sun, Jun 11, 2023 at 1:08 AM jsw2000 @.***> wrote:

Hi, thanks a lot for your question! I've tried reading out the depth data from a HDF5 from that dataset, and I can confirm that it indeed doesn't contain depth data, but rather the RGB data. That doesn't seem right—I'll follow up with some other folks on the project @.*** https://github.com/danielbear).

Re the training files: IIRC we didn't generate the depth data for the training data to keep the size manageable (the HDF5s can be quite large). If you need access to those fields, you could consider regenerating the data with the right flags set. The code to generate the dataset can be found here: https://github.com/neuroailab/tdw_physics/tree/Neurips2021

Thanks for your reply!

— Reply to this email directly, view it on GitHub https://github.com/cogtoolslab/physics-benchmarking-neurips2021/issues/55#issuecomment-1586019362, or unsubscribe https://github.com/notifications/unsubscribe-auth/AA6QMZPY7SYARASWRDLETGLXKVHEZANCNFSM6AAAAAAY7SYGJQ . You are receiving this because you were mentioned.Message ID: @.*** com>