Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
[project page][paper][cite]
overview
demos
cuda 11.1 and pytorch 3.8
preparations
git clone https://github.com/yifita/idf.git
cd idf
# conda environment and dependencies
# update conda
conda update -n base -c defaults conda
# install requirements
conda env create --name idf -f environment.yml
conda activate idf
# download data. This will download 8 mesh and point clouds to data/benchmark_shapes
sh data/get_data.shsurface reconstruction
# surface reconstruction from point cloud
# replace {asian_dragon} with another model name inside the benchmark_shape folder
python net/classes/runner.py net/experiments/displacement_benchmark/ablation/ablation_phased_scaledTanh_yes_act_yes_baseLoss_yes.json --name asian_dragon
detail transfer
This example uses provided base shapes
sh data/get_dt_shapes.sh
# evaluation of the pretrained examples. This will save the results in 'runs/shorts_residual_filmsiren'
python net/classes/runner.py net/experiments/transfer/shorts_2phase.jsonOr you could also train these examples yourselves:
sh data/get_dt_shapes.sh
# this will train the base shapes for the source and target shapes, then train the transferable idf
python net/classes/executor.py net/experiments/transfer/exec.jsonbibtex
@inproceedings{yifan2021geometry,
title={Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields},
author={Yifan, Wang and Rahmann, Lukas and Sorkine-hornung, Olga},
booktitle={International Conference on Learning Representations},
year={2021}
}