Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Related tags

Text Data & NLPidf
Overview

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

[project page][paper][cite]

overview

overview video

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.sh

surface 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
python net/classes/runner.py net/experiments/displacement_benchmark/transfer/shorts_2phase.json

bibtex

@misc{yifan2021geometryconsistent,
      title={Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields},
      author={Wang Yifan and Lukas Rahmann and Olga Sorkine-Hornung},
      year={2021},
      eprint={2106.05187},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Yifan Wang
PhD student @ ETH Zurich
Yifan Wang
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