D3Feat.pytorch
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[PyTorch] Official Implementation of CVPR'20 oral paper - D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features https://arxiv.org/abs/2003.03164
Thanks your work! I want to know how to get the std of FMR in the paper and the rotated experiments? because the test.py cannot generate it. 
Hello, I see that you are using `dist_keypts = cdist(sel_P_src, sel_P_src)` instead of `dist_keypts = cdist(sel_P_src, sel_P_tgt)` when calculating dist_keypts, why is this? Thanks
Hi, I noticed that the registration recall rate was not calculated in test.py, so I calculated it manually. I have two questions: 1. How to calculate the registration recall rate?...
Thanks for your awesome work! Do you mind if I ask to provide the demo code for pytorch?It would help me later apply the industrial data set to the code....
Hi, I was wondering if you validated the correspondences between src and tgt over the trainset. If I understand correctly, the point clouds given in the `.pkl` files are already...
作者您好! 非常感谢您和团队的出色的工作!在尝试使用你们的代码时,我发现预训练模型和数据集的链接失效了,可否重新分享?或者发我邮箱,谢谢!
Could you check the google drive address? Thanks.