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Neural Rendering

Given a representation of a 3D scene of some kind (point cloud, mesh, voxels, etc.), the task is to create an algorithm that can produce photorealistic renderings of this scene from an arbitrary viewpoint. Sometimes, the task is accompanied by image/scene appearance manipulation.

Papers

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Showing 121–130 of 514 papers

TitleStatusHype
CityCraft: A Real Crafter for 3D City Generation—0
GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian SplatsCode2
3D-HGS: 3D Half-Gaussian Splatting—0
GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction—0
Gated Fields: Learning Scene Reconstruction from Gated Videos—0
HFGS: 4D Gaussian Splatting with Emphasis on Spatial and Temporal High-Frequency Components for Endoscopic Scene ReconstructionCode0
Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D PerceptionCode1
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam VideosCode2
F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting—0
Part123: Part-aware 3D Reconstruction from a Single-view Image—0
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