GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [Project Page] [arxiv]
Left to Right: Input, Upsampled, Ours, and GT.
pytorch #1.10.0+cu111 pytorch3d #0.6.2 open3d trimesh point-cloud-utils cd pointnet2_ops_lib python setup.py install Download the data from Google Drive (These shapes are processed by DISN, remove the interior and non-manifold structures.)
Then use the codes in scripts to get the dataset.
python scale_off.py #data_path need to be changed python pds_pc.py python random_pc.py python sample.py python main_pu.py --data_path=your_data_path python main_rec.py --data_path=your_data_path Note: You need to change the data path.
We provide the pretrained model in log_reconstruction_100.00_1.000_0.100 and demo data in test_data, and you can use them to generate meshes
python eval_rec.py --res=128 --input='test_data/shapenet.ply' --output='test_data/shapenet_mesh.ply' python eval_rec.py --res=128 --input='test_data/MGN.ply' --output='test_data/MGN_mesh.ply' python eval_rec.py --res=192 --input='test_data/scene.ply' --output='test_data/scene_mesh.ply' --scale=True If the input point cloud is dense enough and it does not need to be upsampled, you can run the following code
python eval_rec_dense.py --res=128 --input=<path to input mesh> --output=<path to output mesh> @inproceedings{ren2023geoudf, title={Geoudf: Surface reconstruction from 3d point clouds via geometry-guided distance representation}, author={Ren, Siyu and Hou, Junhui and Chen, Xiaodong and He, Ying and Wang, Wenping}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={14214--14224}, year={2023}}