ukcheolshin/thermalmonodepth

Official implementation of the paper "Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion"

thermalmonodepth's Issues

Is this a bug?

in common/data_prepare/prepare_train_data_VIVID.py

For 'indoor_robust_varying_well_lit' sequence, why 'RGB' folder contents and 'Thermal' folder contents are exchanged? Is this a bug?

About hyperparameter settings

Hello
your work is very inspiring.Thank you for sharing!

I found that the learning rate is set to 1e-6 in the paper, but the default parameter of the provided code is set to 1e-4. Similarly, the weight of the smoothing loss is set to 0.1 in the paper, but the default parameter of the provided code is set to 0.01.

Which parameter should be used for better training results?

About Evaluation

Hello

I've seen this interesting research very well.

I'm running the evaluation code and checking the performance, but I don't know what kind of performance is correct to compare with the reported performance.

It is reported that the performance of the Indoor test set (Well-lit) is as follows.

Ours | 0.152 | 0.121 | 0.538 | 0.196 | 0.814 | 0.965 | 0.992

However, when the performance is measured using the uploaded pre-train model, the following performance is shown for the
"indoor robust_varing_well_it" folder.

==> Evaluating depth result...
 Scaling ratios | med: 4.776 | std: 0.056
 Scaling ratios | mean: 4.764 +- std: 0.268

   abs_rel |   sq_rel |     rmse | rmse_log |       a1 |       a2 |       a3 | 
&   0.129  &   0.090  &   0.445  &   0.171  &   0.879  &   0.981  &   0.996  \\

Is it correct to look at the performance of the folder above to see the correct performance?
And in which folder should the "Indoor test set (Low-/Zero-light)" see the performance?

code release

Great job!
When will the code released?
thank you

About dataset structure

The dataset structure you mentioned in README contains some files that are not available in ViViD++Rosbag. For example, cali_ ther_ to_ rgb.yaml，avg_ velocity_ thermal.txt，poses_ thermal.txt. How can I get these documents?

Dataset preparation

Hi,

I noticed that in the common/data_prepare/VIVID_raw_loader.py the validation dataset is defined as part of the test dataset:

self.indoor_train_list = ['indoor_aggresive_global', 'indoor_unstable_local', 'indoor_robust_global', 'indoor_robust_local', 'indoor_unstable_global']
self.indoor_val_list = ['indoor_robust_dark', 'indoor_aggresive_local']
self.indoor_test_list = ['indoor_robust_dark', 'indoor_robust_varying', 'indoor_aggresive_dark', 'indoor_unstable_dark', 'indoor_aggresive_local']
self.outdoor_train_list = ['outdoor_robust_day1', 'outdoor_robust_day2']
self.outdoor_val_list = ['outdoor_robust_night1']
self.outdoor_test_list = ['outdoor_robust_night1', 'outdoor_robust_night2']

Could you please tell me the reason?