https://bitbucket.org/bchradiology/u-net
공식 코드Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
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://bitbucket.org/jkdeng/2sudf
공식 코드https://github.com/10ring/laa-net
공식 코드https://github.com/3dlg-hcvc/3dhoi
공식 코드https://github.com/3dlg-hcvc/mirror3d
공식 코드https://github.com/520jz/spikegs
공식 코드SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream
https://github.com/96lives/ssen
공식 코드Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
https://github.com/AIBluefisher/GraphSfM
공식 코드https://github.com/ActiveVisionLab/MobileBrick
공식 코드MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices
https://github.com/ActiveVisionLab/ray-onet
공식 코드Ray-ONet: Efficient 3D Reconstruction From A Single RGB Image
https://github.com/AlexandreBinninger/TetWeave
공식 코드https://github.com/AljazBozic/DeepDeform
공식 코드DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data
https://github.com/AljazBozic/NeuralGraph
공식 코드Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
https://github.com/AndreeaDogaru/SphereGuided
공식 코드https://github.com/Andy97/DeepMLS
공식 코드Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
https://github.com/AnjieCheng/CanonicalVAE
공식 코드https://github.com/AnnnnnieZhang/M3D
공식 코드https://github.com/AntonFirc/diffusion-deepfake-speech-dataset
공식 코드https://github.com/ArashNasrEsfahani/Object-Rearrangement-in-Dynamic-Environments
공식 코드AI-Driven Relocation Tracking in Dynamic Kitchen Environments
https://github.com/Arlo0o/StereoScene
공식 코드https://github.com/ArthasMil/AACVP-MVSNet
공식 코드Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction
https://github.com/Ashigarg123/ShiftySpeech
공식 코드ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
https://github.com/AstraZeneca/PerfCam
공식 코드PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models
https://github.com/ChenFengYe/AutoSweep
공식 코드AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
https://github.com/ChenqiKONG/OSDFD
공식 코드Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
https://github.com/Chris10M/EventEgo3D
공식 코드EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams
https://github.com/Clench/TAR_resAE
공식 코드TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning
https://github.com/CompVis/taming-transformers
공식 코드https://github.com/ControlNet/LAV-DF
공식 코드https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/Coral79/CDDB
공식 코드A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
https://github.com/Costwen/Ouroboros3D
공식 코드Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion
https://github.com/CyberAgentAILab/SuperNormal
공식 코드SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration
https://github.com/DariusAf/MesoNet
공식 코드https://github.com/DepthAnything/PromptDA
공식 코드Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
https://github.com/EPFL-VILAB/XTConsistency
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.