The following publications are possibly variants of this publication:
- I-Filtering: Implicit Filtering for Learning Neural Distance Functions From 3D Point CloudsShengtao Li, Yudong Liu, Ge Gao, Ming Gu 0001, Yu-Shen Liu. pami, 48(1):109-126, January 2026. [doi]
- Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise MappingBaorui Ma, Yu-Shen Liu, Zhizhong Han. icml 2023: 23338-23357 [doi]
- Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto SurfaceBaorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker. icml 2021: 7246-7257 [doi]
- NeuralTPS: Learning Signed Distance Functions Without Priors From Single Sparse Point CloudsChao Chen, Yu-Shen Liu, Zhizhong Han. pami, 47(1):565-582, January 2025. [doi]
- Fast Learning of Signed Distance Functions From Noisy Point Clouds via Noise to Noise MappingJunsheng Zhou, Baorui Ma, Yu-Shen Liu, Zhizhong Han. pami, 46(12):8936-8953, December 2024. [doi]
- Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid DeformationTakeshi Noda, Chao Chen, Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu, Zhizhong Han. cvpr 2025: 22139-22149 [doi]
- Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning PriorsChao Chen, Yu-Shen Liu, Zhizhong Han. cvpr 2023: 17712-17723 [doi]
- SDFReg: Learning Signed Distance Functions for Point Cloud RegistrationLeida Zhang, Zhengda Lu, Kai Liu, Yiqun Wang 0001. prcv 2025: 550-564 [doi]
- Geometric implicit neural representations for signed distance functionsLuiz Schirmer, Tiago Novello, Vinícius da Silva, Guilherme Schardong, Daniel Perazzo, Hélio Lopes 0001, Nuno Gonçalves 0001, Luiz Velho 0001. cg, 125:104085, 2024. [doi]
- GridPull: Towards Scalability in Learning Implicit Representations from 3D Point CloudsChao Chen, Yu-Shen Liu, Zhizhong Han. iccv 2023: 18276-18288 [doi]