https://github.com/shreyvish5678/advancing-super-resolution-in-neural-radiance-fields-via-variational-diffusion-strategies
공식 코드Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies
Papers with Code 아카이브
Neural Rendering
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shubhendu-jena/neural-mesh-based-graphics
공식 코드https://github.com/silence-tang/artnerf
공식 코드ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
https://github.com/snap-research/NeROIC
공식 코드NeROIC: Neural Rendering of Objects from Online Image Collections
https://github.com/snap-research/mobiler2l
공식 코드https://github.com/sxyu/plenoctree
공식 코드PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/transpchan/Live3D
공식 코드https://github.com/tsattler/meshloc_release
공식 코드https://github.com/tsigalko18/transferability-testing-sdcs
공식 코드https://github.com/tusen-ai/TSTTC
공식 코드TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios
https://github.com/urs-waldmann/netepu
공식 코드https://github.com/vastlik/symformer
공식 코드SymFormer: End-to-end symbolic regression using transformer-based architecture
https://github.com/wenwhx/nerfdirector
공식 코드NeRF Director: Revisiting View Selection in Neural Volume Rendering
https://github.com/wrk226/garmentanimationnerf
공식 코드https://github.com/wrld/uc-nerf
공식 코드UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
https://github.com/xg-chu/gpavatar
공식 코드GPAvatar: Generalizable and Precise Head Avatar from Image(s)
https://github.com/xiaohao-xu/unified-pretrain-ad
공식 코드https://github.com/xingyi-li/icontrol3d
공식 코드iControl3D: An Interactive System for Controllable 3D Scene Generation
https://github.com/xmeng525/NeAT
공식 코드NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images
https://github.com/xubaixinxbx/3dheads
공식 코드https://github.com/xubaixinxbx/high-fidelity-3d-reconstruction-of-human-heads
공식 코드https://github.com/yh-yoon/gmt
공식 코드GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture Transfer
https://github.com/yiqun-wang/hfs
공식 코드HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
https://github.com/yueyin27/refref
공식 코드RefRef: A Synthetic Dataset and Benchmark for Reconstructing Refractive and Reflective Objects
https://github.com/yzheng97/OPONeRF
공식 코드https://github.com/zehao-wang/indoortraj
공식 코드Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
https://github.com/zhaohaoyu376/hfgs
공식 코드https://github.com/zijinwuzijin/dof-nerf
공식 코드https://github.com/zixunh/3DGEER
공식 코드3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians
https://github.com/zju3dv/intrinsicnerf
공식 코드IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
https://github.com/zubair-irshad/shapo
공식 코드ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
https://github.com/zvict/papr
공식 코드https://github.com/zyp123494/dynavol
공식 코드Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
https://gitlab.com/mihaicristianpirvu/pose2rgbd
공식 코드Pose2RGBD. Generating Depth and RGB images from absolute positions
https://gitlab.com/nct_tso_public/surgical-video-sim2real
공식 코드Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data
https://github.com/1716757342/mmsr
SymFormer: End-to-end symbolic regression using transformer-based architecture
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.