{"total":33,"rows":[{"id":"2410.14189","title":"Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set","created":"2024-10-18","cited_by_count":49,"area":null},{"id":"2410.18822","title":"Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis","created":"2024-10-24","cited_by_count":41,"area":null},{"id":"2305.11601","title":"Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment","created":"2023-05-19","cited_by_count":39,"area":null},{"id":"2312.04060","title":"Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching","created":"2023-12-07","cited_by_count":37,"area":null},{"id":"2308.11441","title":"Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection","created":"2023-08-22","cited_by_count":36,"area":null},{"id":"2410.15971","title":"Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly","created":"2024-10-21","cited_by_count":35,"area":null},{"id":"2303.14505","title":"Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors","created":"2023-03-25","cited_by_count":28,"area":null},{"id":"2306.01405","title":"Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping","created":"2023-06-02","cited_by_count":27,"area":null},{"id":"2407.14225","title":"Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping","created":"2024-07-04","cited_by_count":15,"area":null},{"id":"2503.18363","title":"MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction","created":"2025-03-24","cited_by_count":15,"area":null},{"id":"2503.18361","title":"NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction","created":"2025-03-24","cited_by_count":12,"area":null},{"id":"2308.13175","title":"GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds","created":"2023-08-25","cited_by_count":11,"area":null},{"id":"2309.09211","title":"Neural Gradient Learning and Optimization for Oriented Point Normal Estimation","created":"2023-09-17","cited_by_count":11,"area":null},{"id":"2411.01208","title":"MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step","created":"2024-11-02","cited_by_count":11,"area":null},{"id":"2305.05873","title":"Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation","created":"2023-05-10","cited_by_count":10,"area":null},{"id":"2311.00389","title":"NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function","created":"2023-11-01","cited_by_count":10,"area":null},{"id":"2408.05477","title":"Scene123: One Prompt to 3D Scene Generation via Video-Assisted and Consistency-Enhanced MAE","created":"2024-08-10","cited_by_count":7,"area":null},{"id":"2503.23670","title":"Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation","created":"2025-03-31","cited_by_count":7,"area":null},{"id":"2508.05631","title":"GAP: Gaussianize Any Point Clouds with Text Guidance","created":"2025-08-07","cited_by_count":7,"area":null},{"id":"2308.02237","title":"MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning","created":"2023-08-04","cited_by_count":6,"area":null},{"id":"2308.09314","title":"Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation","created":"2023-08-18","cited_by_count":6,"area":null},{"id":"2312.09154","title":"CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry","created":"2023-12-14","cited_by_count":6,"area":null},{"id":"2407.03165","title":"Consistent Point Orientation for Manifold Surfaces via Boundary Integration","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2510.25210","title":"U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching","created":"2025-10-29","cited_by_count":3,"area":null},{"id":"2511.21365","title":"PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation","created":"2025-11-26","cited_by_count":3,"area":null},{"id":"2411.19037","title":"3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes","created":"2024-11-28","cited_by_count":2,"area":null},{"id":"2507.03394","title":"Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2401.05745","title":"Surface Normal Estimation with Transformers","created":"2024-01-11","cited_by_count":1,"area":null},{"id":"2304.04884","title":"PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation","created":"2023-04-10","cited_by_count":0,"area":null},{"id":"2406.00347","title":"E$^3$-Net: Efficient E(3)-Equivariant Normal Estimation Network","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2406.09681","title":"Asymmetrical Siamese Network for Point Clouds Normal Estimation","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2406.18541","title":"Refining 3D Point Cloud Normal Estimation via Sample Selection","created":"2024-05-20","cited_by_count":0,"area":null},{"id":"2409.01100","title":"OCMG-Net: Neural Oriented Normal Refinement for Unstructured Point Clouds","created":"2024-09-02","cited_by_count":0,"area":null}],"next_cursor":null}