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Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment.

, , , and . CVPR, page 17724-17734. IEEE, (2023)

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3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds., , , , , , and . ICRA, page 15416-15423. IEEE, (2024)Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds., , , , and . NeurIPS, (2022)Uni3D: Exploring Unified 3D Representation at Scale., , , , , and . CoRR, (2023)Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching., , , , , and . NeurIPS, (2023)Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping., , , and . CoRR, (2024)GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation., , , , , and . CoRR, (2023)Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection., , , , and . ICCV, page 3158-3169. IEEE, (2023)NeAF: Learning Neural Angle Fields for Point Normal Estimation., , , , and . AAAI, page 1396-1404. AAAI Press, (2023)Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment., , , and . CVPR, page 17724-17734. IEEE, (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)