Table2. citation

[nitba]

Hi @Spurra, Inreagrd with table 2. in your paper, how you get the number of "8.68" from [38] work, in paper I did not found such a number and when I run his 'eval3d.py' provided script I got different number,

Would you please tell me the setup for his code you have used to get this number?

Thanks,

[spurra]

Hi @Ivamcoder, I believe I just ran eval3d.py from the provided code of Zimmermann & Brox and got this number. It may be entirely possible that there may be minor discrepancies due to version differences. What number do you get?

[nitba]

Hi @spurra ,

This is what I get, so far from 8.56 ,

[nitba]

One more question is that in Table2 you have omited Handness which represent the left or right hand, and the scale,

• How you omit the scale , Does it mean that the output hand pose is just palm-relative but not normalized respect to a reference bone? so in this experiment did you use scale Augmentation for data and keeping the hand bounding box in a range? if so would you please confirm me that I get correctly.

and

• if we uniform the size of hand bounding box mentioned in paper as input into network how can we say the output of network has correct scale to be scale invariance , I got confused :(

• when you have used both view of STB dataset , it means that instead of 15K images for training , you have 30K images?

[spurra]

Hi @spurra ,

This is what I get, so far from 8.56 ,

Is this using the ground truth hand cropped images?

One more question is that in Table2 you have omited Handness which represent the left or right hand, and the scale,

* How you omit the scale , Does it mean that the output hand pose is just palm-relative but not normalized respect to a reference bone? so in this experiment did you use `scale Augmentation for data and keeping the hand bounding box in a range`? if so would you please confirm me that I get correctly.

and

* if we uniform the size of  hand bounding box mentioned in paper as input into network  how can we say the output of network  has correct scale to be scale invariance , I got confused :(

* when you have used both view of STB dataset , it means that instead of `15K` images for training , you have  `30K` images?

• Yes I left out the scaling for that experiment. I do not divide by the bone length of the reference bone
• Yes, using both views doubles the number of training data. I do not remember if I used both though.

[nitba]

Hi @spurra ,

Is this using the ground truth hand cropped images?

yes, in eval3d.py , I have BinaryDbReaderSTB(mode='evaluation', shuffle=False, hand_crop=True, use_wrist_coord=False)

[spurra]

I'm not quite sure where the discrepancy comes from. If I find the time, I'll investigate this.

[nitba]

Thank you @spurra