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Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.

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Hi Chris, I noticed that you only include voxels from overlapping volumes of point clouds. i.e only voxels that have at least one correspondence in the other cloud have a...

Hello, thanks for sharing the well-organized code. I have downloaded the training data of 3dmatch from [the link](http://node2.chrischoy.org/data/datasets/registration/threedmatch.tgz) provided in `scripts/download_datasets.sh`. I found there only 7000+ scene pairs with 0.3...

Hi chrischoy, @chrischoy Nice work and thank you for sharing the code. I have a question about the implementation of ResUNet2. In my opinion, the forward() function of ResUNet2 should...

Hi thanks for the amazing work! During the evaluation of pairwise registration, your code seems to compute the nearest neighbour in the second point cloud for each point in the...

Hi Chris, Thank you for sharing the code! I wonder which algorithm is using here for transformation estimation in the util/transform_estimation.py. Can you point out the reference? Thanks! Qiaojun

Hi, It's a very nice work. When I try to experiment with my dataset, my training loss drops, but the evaluation loss doesn't. My dataset is similar to kitti. Can...

Thank you for sharing your advanced work. I met CUDA merge_sort error when training on 3DMatch. **Environment:** ``` Ubuntu 20.04 CUDA 11.1 Python 3.8 MinkwoskiEngine v0.5.3 ``` **command** ```sh python...