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DylanWusee avatar DylanWusee commented on August 29, 2024 2

Hi,

Thank you for your interest in our work.

There are several changes that I made to achieve better performance as bellow:

  1. Train with more dense point clouds. Originally, we use N = 8192 points, now we use N <= 100000 points.

  2. We remove Batch Normalization. We find that batch normalization can give more overfitting in our model.

  3. we use a much deeper network structure. We add 3~5 residual blocks after each downsampling.

We will try to release the related code and the trained model in pytorch as soon as possible.
Please refer: https://github.com/DylanWusee/pointconv_pytorch

Thank you very much.
Wenxuan

from pointconv.

kentang-mit avatar kentang-mit commented on August 29, 2024

Hi Wenxuan,

Thanks a lot for your helpful response. The observation 2 seems to be very interesting~ I'm closing this issue because it has solved my problem.

Best,
Haotian

from pointconv.

linhaojia13 avatar linhaojia13 commented on August 29, 2024

HI, @DylanWusee , do you add RGB channels in the input in your updated model?

from pointconv.

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