the result in the test phase （main_nyu_posereg_embedding） about deep-prior HOT 11 CLOSED

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is the data used for training is "depth_1" in NYU dataset. ? or "synthdepth"?when I used the "depth_1",I found that the code didn't detect the position of the hand very well.when I used the "synthdepth"，the code detected the position of the hand well.Thank you very much.

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The code of hand detector is based on gtorig [13]?which is from the data of label.But there is no such data in practice.How do we cut out the pictures with only hands?Thank you very much.

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is used the main_nyu_com_refine for for refining the location of the hand？then use the main_nyu_posereg_embedding for training the location of the joints，finally use the ORRef for refine the location of the joints？

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I understand. Thanks a lot！

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The test results of the model I trained came out. The test results in test 2 were much worse than those in test 1.Is it because the two data people are different?Does the size of the palm of different people affect the accuracy of the test? the result of your report is frome test1 or test2? look forward to your favourable reply. Thank you!

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distance(mm) | train | test1 | test2

10 | 25.84% | 15.82% | 0.12%
20 | 74.57% | 52.01% | 11.44%
30 | 88.89% | 73.44% | 32.71%
40 | 94.54% | 82.38% | 48.61%
50 | 97.43% | 89.18% | 64.52%
60 | 98.85% | 93.81% | 76.74%
70 | 99.43% | 97.01% | 85.31%
80 | 99.76% | 98.44% | 90.31%

This is the result of my test（the fraction of frames Within distance(max)）

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You are correct that the results for test2 are worse. This is due to the fact that test2 is a different user with different hand size than the training user. Therefore, it is encouraged to adjust the crop size of the hand cube accordingly. The evaluation in the report is from the joint set of test1+test2.

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