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insight - Neural Implicit Surface Reconstruction
UFORecon: Generalizable Sparse-View Surface Reconstruction Framework
The author introduces UFORecon, a robust view-combination generalizable surface reconstruction framework that outperforms existing methods in both favorable and unfavorable scenarios.
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and Unfavorable Data Sets
提案されたUFOReconフレームワークは、交差ビューマッチング機能を活用して画像間の相関を学習し、明示的な2Dマッチング類似性をエンコードすることで、従来の表面再構築手法よりも優れたパフォーマンスを実現します。
Hybrid Directional Parameterization for Robust Neural Implicit Surface Reconstruction
A novel hybrid directional parameterization that effectively combines the advantages of viewing and reflection directions to handle both specular surfaces and complex structures in neural implicit surface reconstruction.
AniSDF: Accurate 3D Reconstruction and High-Fidelity Rendering with Anisotropic Neural Surfaces
AniSDF presents a unified approach that learns fused-granularity neural surfaces with anisotropic encoding to achieve high-quality 3D reconstruction and photorealistic rendering.
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