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GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation.

, , , , , and . CoRR, (2023)

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Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds., , , , and . NeurIPS, (2022)Uni3D: Exploring Unified 3D Representation at Scale., , , , , and . CoRR, (2023)Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling., , , , and . AAAI, page 3181-3189. AAAI Press, (2024)NeAF: Learning Neural Angle Fields for Point Normal Estimation., , , , and . AAAI, page 1396-1404. AAAI Press, (2023)Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 23338-23357. PMLR, (2023)GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation., , , , , and . CoRR, (2023)Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 7246-7257. PMLR, (2021)Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection., , , , and . ICCV, page 3158-3169. IEEE, (2023)Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment., , , and . CVPR, page 17724-17734. IEEE, (2023)Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces., , , and . CoRR, (2020)