Entity · ≈ 1 min read
PSNR
Incoming relations
- uses_metric → 2D Gaussian Splatting for Geometrically Accurate Radiance Fields (Paper)
- uses_metric → AGG: Amortized Generative 3D Gaussians for Single Image to 3D (Paper)
- uses_metric → About graphdeco-inria/gaussian-splatting (Repository)
- uses_metric → About nerfstudio-project/gsplat (Repository)
- uses_metric → BARF: Bundle-Adjusting Neural Radiance Fields (Paper)
- uses_metric → Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM (Paper)
- uses_metric → DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras (Paper)
- uses_metric → DeepV2D: Video to Depth with Differentiable Structure from Motion (Paper)
- uses_metric → Direct Sparse Odometry (Paper)
- uses_metric → DreamFusion: Text-to-3D using 2D Diffusion (Paper)
- uses_metric → DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation (Paper)
- uses_metric → Dynamic View Synthesis from Dynamic Monocular Video (Paper)
- uses_metric → Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields (Paper)
- uses_metric → GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting (Paper)
- uses_metric → GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting (Paper)
- uses_metric → Gaussian Splatting SLAM (Paper)
- uses_metric → Implicit Geometric Regularization for Learning Shapes (Paper)
- uses_metric → Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Paper)
- uses_metric → Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model (Paper)
- uses_metric → K-Planes: Explicit Radiance Fields in Space, Time, and Appearance (Paper)
- uses_metric → LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation (Paper)
- uses_metric → LRM: Large Reconstruction Model for Single Image to 3D (Paper)
- uses_metric → LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes (Paper)
- uses_metric → MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes (Paper)
- uses_metric → Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors (Paper)
Connected node types
- Paper: 44
- Repository: 2
Mentions in the corpus
- uses_metric Direct Sparse Odometry
- uses_metric DeepV2D: Video to Depth with Differentiable Structure from Motion
- uses_metric Implicit Geometric Regularization for Learning Shapes
- uses_metric NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
- uses_metric NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
- uses_metric Nerfies: Deformable Neural Radiance Fields
- uses_metric Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
- uses_metric BARF: Bundle-Adjusting Neural Radiance Fields
- uses_metric UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
- uses_metric Dynamic View Synthesis from Dynamic Monocular Video
- uses_metric NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
- uses_metric Volume Rendering of Neural Implicit Surfaces
- uses_metric DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
- uses_metric Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
- uses_metric Plenoxels: Radiance Fields without Neural Networks
- uses_metric NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
- uses_metric Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
- uses_metric MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
- uses_metric DreamFusion: Text-to-3D using 2D Diffusion
- uses_metric Magic3D: High-Resolution Text-to-3D Content Creation
- uses_metric K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
- uses_metric MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
- uses_metric Zero-1-to-3: Zero-shot One Image to 3D Object
- uses_metric Point-SLAM: Dense Neural Point Cloud-based SLAM
- uses_metric Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
- uses_metric ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
- uses_metric Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
- uses_metric DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
- uses_metric LRM: Large Reconstruction Model for Single Image to 3D
- uses_metric Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model
- uses_metric GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
- uses_metric SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
- uses_metric LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes
- uses_metric Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
- uses_metric Mathematical Supplement for the $\\texttt{gsplat}$ Library
- uses_metric Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields
- uses_metric Gaussian Splatting SLAM
- uses_metric AGG: Amortized Generative 3D Gaussians for Single Image to 3D
- uses_metric LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
- uses_metric GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting
- uses_metric TripoSR: Fast 3D Object Reconstruction from a Single Image
- uses_metric latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction
- uses_metric 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
- uses_metric Revising Densification in Gaussian Splatting
- uses_metric About graphdeco-inria/gaussian-splatting
- uses_metric About nerfstudio-project/gsplat
Cross-references in raw data
examples/demo-corpus/data/research/papers/arxiv-1607-02565/paper.mdexamples/demo-corpus/data/research/papers/arxiv-1812-04605/abstract.mdexamples/demo-corpus/data/research/papers/arxiv-1812-04605/paper.mdexamples/demo-corpus/data/research/papers/arxiv-2002-10099/abstract.mdexamples/demo-corpus/data/research/papers/arxiv-2002-10099/paper.mdexamples/demo-corpus/data/research/papers/arxiv-2003-08934/abstract.mdexamples/demo-corpus/data/research/papers/arxiv-2003-08934/paper.mdexamples/demo-corpus/data/research/papers/arxiv-2008-02268/abstract.mdexamples/demo-corpus/data/research/papers/arxiv-2008-02268/paper.mdexamples/demo-corpus/data/research/papers/arxiv-2011-12948/abstract.mdexamples/demo-corpus/data/research/papers/arxiv-2011-12948/paper.mdexamples/demo-corpus/data/research/papers/arxiv-2103-13415/abstract.md
Source provenance
examples/demo-corpus/data/research/papers/arxiv-1607-02565/abstract.md