RepSurf
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[CVPR 2022 Oral] Official implementation for "Surface Representation for Point Clouds"
RepSurf - Surface Representation for Point Clouds
[CVPR 2022 Oral]
By Haoxi Ran* , Jun Liu, Chengjie Wang ( * : corresponding contact)
The pytorch official implementation of "Surface Representation for Point Clouds". The repos of RepSurf on other tasks are coming:
Other Tasks:
3D Segmentation (ongoing): RepSurf for Segmentation
Preparation
Environment
We tested under the environment:
- python 3.7
- pytorch 1.6.0
- cuda 10.1
- gcc 7.2.0
- h5py
For anaconda user, initialize the environment by:
sh init.sh
Or you can manually install the above packages and compile the cuda-based point operators by:
cd modules/pointops
python3 setup.py install
Classification
ScanObjectNN
- Performance:
| Model | Accuracy | #Params | Augment | Code | Log | Checkpoint |
|---|---|---|---|---|---|---|
| MVTN | 82.8 | 4.24M | None | link | N/A | link |
| PointMLP | 85.7 | 12.6M | Scale, Shift | link | link | link |
| PointNet++ SSG | 77.9 | 1.475M | Rotate, Jitter | link | N/A | N/A |
| Umbrella RepSurf (PointNet++ SSG) | 84.87 | 1.483M | None | link | google drive | google drive (6MB) |
| Umbrella RepSurf (PointNet++ SSG, 2x) | 86.05 | 6.806M | None | link | google drive | google drive (27MB) |
- To download dataset:
wget http://download.cs.stanford.edu/orion/scanobjectnn/h5_files.zip
unzip h5_files.zip
ln -s [PATH]/h5_files data/ScanObjectNN
Note: We conduct all experiments on the hardest variant of ScanObjectNN (PB_T50_RS).
- To train Umbrella RepSurf on ScanObjectNN:
sh scripts/repsurf/scanobjectnn/repsurf_ssg_umb.sh
- To train Umbrella RepSurf (2x setting) on ScanObjectNN:
sh scripts/repsurf/scanobjectnn/repsurf_ssg_umb_2x.sh
Visualization
We provide several visualization results in the folder ./visualization for a closer look at the construction of RepSurf.
TODO
- [ ] Classification on ModelNet40
- [ ] Segmentation on S3DIS / ScanNet
Acknowledgment
We use part of the library pointops from PointWeb.
License
RepSurf is under the Apache-2.0 license. Please contact the primary author Haoxi Ran ([email protected]) for commercial use.
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