https://github.com/chenguolin/DiffSplat
SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/chrischoy/3D-R2N2
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/chrischoy/open-ucn
ASLFeat: Learning Local Features of Accurate Shape and Localization
https://github.com/christophreich1996/3d_baggage_segmentation
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/christophreich1996/oss-net
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/cmhungsteve/Awesome-Transformer-Attention
https://github.com/code-implementation1/Code3/tree/main/eppmvsnet
https://github.com/colmap/colmap
https://github.com/crazyleg/workshop-3d-neural
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/crs4/AtlantaNet
https://github.com/czq142857/BSP-NET-pytorch
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
https://github.com/czq142857/im-net
https://github.com/czq142857/im-net-pytorch
https://github.com/dadung/G2D
https://github.com/dahliau/DPDist
PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/deepfakes/faceswap
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/dengzhi-ustc/a-robust-registration-loss
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
https://github.com/dimtziwnas/InHandScanningICCV15_GroundTruthViewer
https://github.com/dimtziwnas/InHandScanningICCV15_HandMotionViewer
https://github.com/dkogan/mrgingham
https://github.com/dome272/VQGAN
https://github.com/dome272/vqgan-pytorch
https://github.com/donnyruixu/pc-elm-ae
https://github.com/doublez0108/geomvsnet
https://github.com/drexubery/viewcrafter
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
https://github.com/ebnerluca/uw_depth
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets
https://github.com/eigenvivek/diffdrr
https://github.com/endomapper/em_dataset-photometriccalibration
Photometric single-view dense 3D reconstruction in endoscopy
https://github.com/envision-research/lotus
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
https://github.com/ethz-asl/glocal_exploration
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments
https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
https://github.com/fabio-sim/LightGlue-ONNX
https://github.com/facebookresearch/House3D
https://github.com/facebookresearch/SparseConvNet
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
https://github.com/facebookresearch/co-tracker
https://github.com/felix-petersen/gendr
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.