Novel View Synthesis
326 papers with code • 17 benchmarks • 33 datasets
Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.
See Wiki for more introdcutions.
The Synthesis method include: NeRF, MPI and so on.
( Image credit: Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence )
Benchmarks
These leaderboards are used to track progress in Novel View Synthesis
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Libraries
Use these libraries to find Novel View Synthesis models and implementationsDatasets
KITTI
NeRF
ShapeNet
SYNTHIA
KITTI-360
BlendedMVS
Tanks and Temples
Latest papers
Map-Relative Pose Regression for Visual Re-Localization
We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion.
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation
In this work, we propose to fuse the scene reconstruction from multiple images and distill this knowledge into a more accurate single-view scene reconstruction.
G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images
llrtt/G-NeRF
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Novel view synthesis aims to generate new view images of a given view image collection.
Gaussian Pancakes: Geometrically-Regularized 3D Gaussian Splatting for Realistic Endoscopic Reconstruction
Within colorectal cancer diagnostics, conventional colonoscopy techniques face critical limitations, including a limited field of view and a lack of depth information, which can impede the detection of precancerous lesions.
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
ispc-lab/lidar4d
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In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis.
Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects
To clearly reconstruct dynamic scenes, we propose a new framework by considering two frames at a time. We pretrain a NeRF model for an articulated object. When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state by incorporating past knowledge in the pretrained NeRF model with minimal observations in the present state.
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis.
GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
Mitigating Motion Blur in Neural Radiance Fields with Events and Frames
Neural Radiance Fields (NeRFs) have shown great potential in novel view synthesis.
DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing
3D Gaussian splatting, a novel differentiable rendering technique, has achieved state-of-the-art novel view synthesis results with high rendering speeds and relatively low training times.
