Closed glorioushonor closed 1 year ago
You could locate the problematic subject by try-catch
. The evaluation works well on my end on the same data.
You could locate the problematic subject by
try-catch
. The evaluation works well on my end on the same data.
This error is quite strange; when I train again, sometimes this error does not occur, but the testing results show a discrepancy of 0.1cm compared to the data in the paper, which is a significant difference. The cause of this error is also attributed to the use of pytorch3d==0.7.1. When using pytorch3d==0.7.5, a segmentation fault occurs. Therefore, pytorch==0.7.2 is recommended for use. In the end, testing with the pre-trained model yields results with a maximum difference of only 0.003 cm.
Thank you for your excellent work, but I noticed an error occurring during the testing phase. Could it be that the Cape dataset is corrupted? I've tried re-downloading the Cape dataset, but it doesn't seem to resolve the issue.