https://github.com/nitinagarwal/GAMesh
공식 코드GAMesh: Guided and Augmented Meshing for Deep Point Networks
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 10
- 보존된 평가 기록
- 44
3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nitinagarwal/QuadricLoss
공식 코드https://github.com/nnanhuang/s3gaussian
공식 코드$\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
https://github.com/noahstier/vortx
공식 코드VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion
https://github.com/nopqaq/test3r
공식 코드https://github.com/nowindbutrain/blockshufflelearning
공식 코드https://github.com/nsavinov/ray_potentials
공식 코드https://github.com/nubot-nudt/td-nerf
공식 코드TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization
https://github.com/nuriachandra/deepfake-eval-2024
공식 코드Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
https://github.com/nv-nguyen/gigapose
공식 코드GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
https://github.com/nv-tlabs/DefTet
공식 코드Learning Deformable Tetrahedral Meshes for 3D Reconstruction
https://github.com/nv-tlabs/atiss
공식 코드ATISS: Autoregressive Transformers for Indoor Scene Synthesis
https://github.com/nvlabs/3dgm
공식 코드Memorize What Matters: Emergent Scene Decomposition from Multitraverse
https://github.com/nvlabs/instant-ngp
공식 코드Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/nvlabs/tiny-cuda-nn
공식 코드Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/nvlabs/voxformer
공식 코드VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
https://github.com/nwuzhouwei/ssmp
공식 코드https://github.com/nyu-systems/grendel-gs
공식 코드https://github.com/oidelima/Deepfake-Detection
공식 코드Deepfake Detection using Spatiotemporal Convolutional Networks
https://github.com/omiksik/dfusion
공식 코드https://github.com/ondyari/FaceForensics
공식 코드FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/opendatalab/mls-brn
공식 코드3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions
https://github.com/oppo-us-research/planemvs
공식 코드https://github.com/orchidchetiaphukan/multilingualptm_add_naacl24
공식 코드https://github.com/pagraf/swin-bathyunet
공식 코드https://github.com/palanglois/line-surface-reconstruction
공식 코드https://github.com/parskatt/dedode
공식 코드DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
https://github.com/parul-gupta/multifakeverse
공식 코드https://github.com/paschalidoud/hierarchical_primitives
공식 코드Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
https://github.com/paschalidoud/raynet
공식 코드RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
https://github.com/paul007pl/MVP_Benchmark
공식 코드https://github.com/pcl3dv/dunerf
공식 코드https://github.com/petrikvladimir/video_skills_learning_with_approx_physics
공식 코드Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
https://github.com/petrikvladimir/video_skills_learning_with_aprox_physics
공식 코드Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
https://github.com/phasetorch/phasetorch
공식 코드https://github.com/piotrkawa/attack-agnostic-dataset
공식 코드Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.