Papers with Code 아카이브
Multi-View 3D Reconstruction
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction
Regist3R: Incremental Registration with Stereo Foundation Model
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction
Sub-Image Recapture for Multi-View 3D Reconstruction
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization
Refine3DNet: Scaling Precision in 3D Object Reconstruction from Multi-View RGB Images using Attention
Poster: Reliable 3D Reconstruction for Ad-hoc Edge Implementations
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
PhyRecon: Physically Plausible Neural Scene Reconstruction
SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration
RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
Rethinking Directional Integration in Neural Radiance Fields
GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
Implicit Shape and Appearance Priors for Few-Shot Full Head Reconstruction
PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction
A Theory of Topological Derivatives for Inverse Rendering of Geometry
Long-Range Grouping Transformer for Multi-View 3D Reconstruction
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
PAPR: Proximity Attention Point Rendering
End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
Hierarchical Prior Mining for Non-local Multi-View Stereo
HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces
Multi-View Mesh Reconstruction with Neural Deferred Shading
GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff
Sphere-Guided Training of Neural Implicit Surfaces
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction
A Real World Dataset for Multi-view 3D Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.