https://github.com/ganlab/ostra
공식 코드A One Stop 3D Target Reconstruction and multilevel Segmentation Method
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/google-research/corenet
공식 코드CoReNet: Coherent 3D scene reconstruction from a single RGB image
https://github.com/hongxiaoy/ISO
공식 코드https://github.com/hs-yn/daps
공식 코드Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal Distillation
https://github.com/huiyu-gao/visfusion
공식 코드VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos
https://github.com/hustvl/symphonies
공식 코드Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
https://github.com/j-alex-hanson/speedy-splat
공식 코드Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
https://github.com/jho-yonsei/comogaussian
공식 코드CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
https://github.com/jiaweihe1996/ba-det
공식 코드3D Video Object Detection with Learnable Object-Centric Global Optimization
https://github.com/jkulhanek/wild-gaussians
공식 코드https://github.com/limacv/Deblur-NeRF
공식 코드https://github.com/liuliu3dv/GOC
공식 코드Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion
https://github.com/mhayoz/online_endo_track
공식 코드Online 3D reconstruction and dense tracking in endoscopic videos
https://github.com/mkhangg/refined_panoptic_mapping
공식 코드Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots
https://github.com/mordecaimalignatius/gafar
공식 코드https://github.com/nihalsid/retrieval-fuse
공식 코드RetrievalFuse: 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/nvlabs/voxformer
공식 코드VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
https://github.com/ohsngjun/3D-4DGS
공식 코드Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
https://github.com/pkqbajng/cgformer
공식 코드Context and Geometry Aware Voxel Transformer for Semantic Scene Completion
https://github.com/princeton-vl/DeepV2D
공식 코드DeepV2D: Video to Depth with Differentiable Structure from Motion
https://github.com/rebecca0011/uavstereo
공식 코드UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios
https://github.com/remadaher/shades
공식 코드SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition
https://github.com/ruili3/Know-Your-Neighbors
공식 코드Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
https://github.com/shiran-yuan/slimmerf
공식 코드https://github.com/shreyesss/hdrsplat
공식 코드HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images
https://github.com/sjtu-deepvisionlab/laga
공식 코드Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
https://github.com/vilab-ucsd/photoscene
공식 코드PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
https://github.com/waterljwant/SSC
공식 코드Anisotropic Convolutional Networks for 3D Semantic Scene Completion
https://github.com/wengflow/robust-e-nerf
공식 코드Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion
https://github.com/whuwuteng/aerial_stereo_dataset
공식 코드https://github.com/xheon/panoptic-reconstruction
공식 코드https://github.com/xianzuwu/Niagara
공식 코드Niagara: Normal-Integrated Geometric Affine Field for Scene Reconstruction from a Single View
https://github.com/yanx27/JS3C-Net
공식 코드https://github.com/zhangyp15/occformer
공식 코드OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
https://github.com/zju3dv/NeuralRecon
공식 코드NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.