Cannot get the same camera pose result as in the paper

[BiancaBing]

Hi Yinyu:
I am now trying to train the layout estimation model but cannot get the same pitch and roll error as in the paper. Also I have tested the pretrained model that is provided in your repository and I got pitch error = 6.3016, roll error = 4.2779 that is not so good as in the paper (pitch error = 3.15, roll error = 2.09). I also tried the same training process as described in the paper with batch size of 32 and lr at 1e-3 (scaled by 0.5 for every 20 epochs) and in the coop repository with batch size of 32 and lr at 1e-4 ( scaled by 0.7 for every 10 epochs and finally remains at 1e-5). However, the best result I got is pitch error = 5.6497, roll error = 3.9949. I have no idea what happened.
Also, the loss curve of test loss starts to increase only after a few epochs as shown below (accuracy curve here indicates the average pitch error, roll error and the sum of pitch & roll):



How can I get the same performance as in the paper?

[yinyunie]

Hi,

We inherit the layout estimation network (LEN) and corresponding loss from CooP paper. If you only train LEN and remove other modules, our network is exactly the LEN in CooP paper. I think you can check if you can get similar results with the CooP method first, and see if there are any bugs or errors.

Best,
Yinyu

[BiancaBing]

Hi,

Thank you for your advice. I have tried the code from CooP and got the results. However, I still have some questions. First of all, I found the BIN value you use is different from CooP. It seems that you multiply the BIN by pi/180. Also, the test and train list you use is also different from CooP. Can I ask which benchmark you used when comparing the results in your paper?