This is the official Pytorch implementation of the paper:
ARO-Net: Learning Implicit Fields from Anchored Radial Observations. CVPR. 2023.
Yizhi Wang*, Zeyu Huang*, Ariel Shamir, Hui Huang, Hao Zhang, Ruizhen Hu.
Paper: arxiv Homepage: ARO-Net Video: Youtube
To setup a conda environment, and build dependencies:
# create conda environment for ARO-Net
conda create -n aro-net python=3.9 pytorch-gpu -c conda-forge
conda activate aro-net
pip install trimesh open3d tensorboard Cython
# install tools from ConvONet
python setup.py build_ext --inplace
# install LFD metrics
git clone https://github.com/kacperkan/light-field-distance
cd light-field-distance
python setup.py install
The dataset (~20GB) used in our experiments can be found in OneDrive and Baidu Disk (Password: so9v).
It contains ABC dataset (the first Chunk), and ShapeNet (Chairs and Airplanes).
The layout of data dir is:
ARO-Net
├── data
│ ├── abc
│ │ │──00_meshes
│ │ │──01_pcds
│ │ │──02_qry_pts
│ │ │──03_qry_dists
│ │ │──04_splits
├── shapenet
│ │ │──00_meshes
│ │ │ │──02691156
│ │ │ │──03001627
│ │ │──01_pcds
│ │ │──02_qry_pts
│ │ │──03_qry_dists
│ │ │──04_splits
├── anchors
We followed the script in Points2Surf to sample qry_pts and calculate their signed distance. Note that we did not use their way of normalization, our way of normalization can be found in this link, where the length of body diagonal of 3D bounding box is normalized to 1.
We provide pre-trained models on ABC dataset (first chunk) and ShapeNet (chairs):
- ABC: OneDrive or Baidu Disk (Password: hcwk).
- ShapeNet: Onedrive or Baidu Disk (Password: x6cj).
Put them under the folder experiments. For ShapeNet dataset, we trained ARO-Net on two kinds of query-occupancy ground-truth provied by IM-Net and OCC-Net, respectively.
To test our pretrained models,
# ABC
python reconstruct.py --name_exp pretrained_abc --name_ckpt aronet_abc.ckpt --name_dataset abc --use_dist_hit --n_pts_test 2048
# ShapeNet Chair (trained w/ IM-Net data)
python reconstruct.py --name_exp pretrained_chairs --name_ckpt aronet_chairs_gt_imnet.ckpt --name_dataset shapenet --categories_test 03001627, --use_dist_hit --n_pts_test 2048 --mc_threshold 0.5
# ShapeNet Chair (trained w/ OCC-Net data)
python reconstruct.py --name_exp pretrained_chairs --name_ckpt aronet_chairs_gt_occnet.ckpt --name_dataset shapenet --categories_test 03001627, --norm_coord --n_pts_test 2048 --mc_threshold 0.2
# ShapeNet Airplane (trained w/ IM-Net data)
python reconstruct.py --name_exp pretrained_chairs --name_ckpt aronet_chairs_gt_imnet.ckpt --name_dataset shapenet --categories_test 02691156, --use_dist_hit --n_pts_test 2048 --mc_threshold 0.5
# ShapeNet Airplane (trained w/ OCC-Net data)
python reconstruct.py --name_exp pretrained_chairs --name_ckpt aronet_chairs_gt_occnet.ckpt --name_dataset shapenet --categories_test 02691156, --norm_coord --n_pts_test 2048 --mc_threshold 0.2
You can also modify --n_pts_test to set the input of points of objects, we pre-sampled 1024 and 2048 points from meshes for testing.
We use Fibonacci sampling to generate 48 anchors for our ARO-Net. Other anchor settings can generated with gen_anc.py.
To train ARO-Net on ABC dataset or ShapeNet:
python train.py --name_exp base_model_abc --name_dataset abc --use_dist_hit --norm_coord
python train.py --name_exp base_model_chairs --name_dataset shapenet --categories_train 03001627, --norm_coord --gt_source occnet
It is recommended to set use_dist_hit to True when training on abc dataset or shapenet (im-net gt) (an auxiliary loss to predict anchor-query to surface distance) when training (it will bring some marginal performance gain). To use this auxiliary loss, first run cal_hit_dist.py.
To train ARO-Net on single shape with data augmentation:
python train.py --name_exp base_model --name_dataset single --name_single fertility
Check all training options in options.py. You need one NVIDIA A100 (80G) to train ARO-Net under the configurations in options.py. You can set the n_bs and n_qry to fit to your GPU capacity. set n_bs to 4 and n_qry to 256 will cost ~20GB video memory.
To reconstruct meshes on test sets:
# ABC
python reconstruct.py --name_exp base_model_abc --name_ckpt 600_301101_xxx_xxx.ckpt --name_dataset abc --use_dist_hit
# ShapeNet Chair
python reconstruct.py --name_exp base_model_chairs --name_ckpt 600_301101_xxx_xxx.ckpt --name_dataset shapenet --categories_test 03001627, --norm_coord
# ShapeNet Airplane
python reconstruct.py --name_exp base_model_chairs --name_ckpt 600_301101_xxx_xxx.ckpt --name_dataset shapenet --name_dataset 02691156, --norm_coord
To evalute HD, CD, and IoU:
# ABC
python eval_metrics.py --name_exp base_model_abc --name_dataset abc
# ShapeNet
python eval_metrics.py --name_exp base_model_chairs --name_dataset shapenet --categories_test 03001627,
python eval_metrics.py --name_exp base_model_chairs --name_dataset shapenet --categories_test 02691156,
To evaluate LDF:
# ABC
python eval_lfd.py --name_exp 202203014_pretrained --name_dataset abc
We use light-field-distance to compute LDF. The implementation of this library prevents us from computing this metric in parallel. It also requires an OpenGL context, so a pyhsical display is recommended.