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Xu Ma 2021-11-13 03:49:30 -05:00 committed by GitHub
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@ -7,6 +7,11 @@ Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual M
Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extract local features using residual point MLP blocks. In each stage, we first transform local point using a geometric affine module, then local points are are extracted before and after aggregation respectively. By repeating multiple stages, PointMLP progressively enlarge the receptive field and model entire point cloud geometric information. Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extract local features using residual point MLP blocks. In each stage, we first transform local point using a geometric affine module, then local points are are extracted before and after aggregation respectively. By repeating multiple stages, PointMLP progressively enlarge the receptive field and model entire point cloud geometric information.
## For rebuttal
We will upload the trained models and log files on Nov/14.
## Pre-trained models ## Pre-trained models
Please download the pre-trained models and log files here: [[anonymous google drive]](https://drive.google.com/drive/folders/1Jn9HNpPsrq-1XqSmOUtw4cwPMjsIiIpz?usp=sharing) Please download the pre-trained models and log files here: [[anonymous google drive]](https://drive.google.com/drive/folders/1Jn9HNpPsrq-1XqSmOUtw4cwPMjsIiIpz?usp=sharing)