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Deep Implicit Surface Point Prediction Networks
Project Page | Paper
<img src="single_shape_csp/media/combined.gif" alt="Lion" width="400" height="400"/> <!--![Lion](single_shape_csp/media/lion_512.gif =256x256) ![Rosa](single_shape_csp/media/rosa_512.gif =256x256)-->If you find our code or paper useful, please cite as
@InProceedings{Venkatesh_2021_ICCV,
author = {Venkatesh, Rahul and Karmali, Tejan and Sharma, Sarthak and Ghosh, Aurobrata and Babu, R. Venkatesh and Jeni, Laszlo A. and Singh, Maneesh},
title = {Deep Implicit Surface Point Prediction Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {12653-12662}
}
Setting up environment
Create an anaconda environment called mesh_funcspace
using
conda env create -f environment.yml
conda activate mesh_funcspace
Single Shape CSP
Training
To train a on a new 3D shape, run
python single_shape_csp/pt_pred/train.py -ename=EXP_NAME -infile=OFF_FILE_PATH
for eg.
python single_shape_csp/pt_pred/train.py -ename=lion -infile=./single_shape_csp/data/lion.off
Evaluation
For evaluation of the models, run:
python single_shape_csp/pt_pred/test.py -ename=EXP_NAME -model_iter=MODEL_ITER -reverse -num_views=NUM_VIEWS
where EXP_NAME
is the experiment name to pick the weights from, MODEL_ITER
is the checkpoint in EXP NAME
(which can can be found in single_shape_csp/weights/EXP_NAME/MODEL_ITER.pt
), and NUM_VIEWS
is number of azimuths to uniformly sample for multi-view rendering.
The generated files can be found at single_shape_csp/videos/EXP_NAME
.
Multi-shape CSP
Multi-shape CSP code can be found in surface_recon.