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Surface Reconstruction

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Showing 451–475 of 624 papers

TitleStatusHype
Discontinuity-preserving Normal Integration with Auxiliary Edges—0
DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization—0
Drone-based Volume Estimation in Indoor Environments—0
Dynamic Fluid Surface Reconstruction Using Deep Neural Network—0
Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface—0
Dynamic Point Cloud Denoising via Gradient Fields—0
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid—0
Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction—0
EC-Net: an Edge-aware Point set Consolidation Network—0
Edge Preserving Implicit Surface Representation of Point Clouds—0
Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo—0
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery—0
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding—0
Enhancing Neural Rendering Methods with Image Augmentations—0
Error Propagation in Satellite Multi-image Geometry—0
EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction—0
Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization—0
Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fields—0
Exploiting Semantic Scene Reconstruction for Estimating Building Envelope Characteristics—0
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects—0
FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering—0
Filling missing data in point clouds by merging structured and unstructured point clouds—0
Fine-detailed Neural Indoor Scene Reconstruction using multi-level importance sampling and multi-view consistency—0
Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views—0
From Flat to Spatial: Comparison of 4 methods constructing 3D, 2 and 1/2D Models from 2D Plans with neural networks—0
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