Papers with Code 아카이브

3D Scene Reconstruction

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Creating 3D scene either using conventional SFM pipelines or latest deep learning approaches.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hs-yn/daps

공식 코드

Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal Distillation

https://github.com/huiyu-gao/visfusion

공식 코드

VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos

https://github.com/j-alex-hanson/speedy-splat

공식 코드

Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives

https://github.com/jho-yonsei/comogaussian

공식 코드

CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images

https://github.com/jiaweihe1996/ba-det

공식 코드

3D Video Object Detection with Learnable Object-Centric Global Optimization

https://github.com/jkulhanek/wild-gaussians

공식 코드

WildGaussians: 3D Gaussian Splatting in the Wild

https://github.com/limacv/Deblur-NeRF

공식 코드

Deblur-NeRF: Neural Radiance Fields from Blurry Images

https://github.com/liuliu3dv/GOC

공식 코드

Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion

https://github.com/mhayoz/online_endo_track

공식 코드

Online 3D reconstruction and dense tracking in endoscopic videos

https://github.com/mkhangg/refined_panoptic_mapping

공식 코드

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots

https://github.com/mordecaimalignatius/gafar

공식 코드

GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm

https://github.com/nihalsid/retrieval-fuse

공식 코드

RetrievalFuse: Neural 3D Scene Reconstruction with a Database

https://github.com/nnanhuang/s3gaussian

공식 코드

$\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving

https://github.com/ohsngjun/3D-4DGS

공식 코드

Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation

https://github.com/princeton-vl/DeepV2D

공식 코드

DeepV2D: Video to Depth with Differentiable Structure from Motion

https://github.com/rebecca0011/uavstereo

공식 코드

UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios

https://github.com/remadaher/shades

공식 코드

SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition

https://github.com/ruili3/Know-Your-Neighbors

공식 코드

Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning

https://github.com/shiran-yuan/slimmerf

공식 코드

SlimmeRF: Slimmable Radiance Fields

https://github.com/shreyesss/hdrsplat

공식 코드

HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images

https://github.com/sjtu-deepvisionlab/laga

공식 코드

Tackling View-Dependent Semantics in 3D Language Gaussian Splatting

https://github.com/vilab-ucsd/photoscene

공식 코드

PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes

https://github.com/wengflow/robust-e-nerf

공식 코드

Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion

https://github.com/whuwuteng/aerial_stereo_dataset

공식 코드

An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset

https://github.com/xheon/panoptic-reconstruction

공식 코드

Panoptic 3D Scene Reconstruction From a Single RGB Image

https://github.com/xianzuwu/Niagara

공식 코드

Niagara: Normal-Integrated Geometric Affine Field for Scene Reconstruction from a Single View

https://github.com/zju3dv/NeuralRecon

공식 코드

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

https://github.com/zju3dv/manhattan_sdf

공식 코드

Neural 3D Scene Reconstruction with the Manhattan-world Assumption

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.