Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 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/alibaba/EasyNLP/tree/master/examples/text2image_generation
https://github.com/aliyoussef97/silk-prp
https://github.com/aljazbozic/transformerfusion
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
https://github.com/ammarahhashmi/Multimodal-Forgery-Detection-Using-Ensemble-Learning
https://github.com/apchenstu/mvsnerf
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
https://github.com/ashawkey/stable-dreamfusion
RealFusion: 360° Reconstruction of Any Object from a Single Image
https://github.com/ashawkey/torch-ngp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/ashikkapadiya/Video-Motion-Retargeting
Learning Character-Agnostic Motion for Motion Retargeting in 2D
https://github.com/astra-vision/PaSCo
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
https://github.com/astra-vision/lmscnet
https://github.com/astra-vision/monoscene
https://github.com/audio-df-ucb/clonedvoicedetection
https://github.com/autonomousvision/campari
https://github.com/autonomousvision/giraffe
https://github.com/autonomousvision/lara
https://github.com/autonomousvision/monosdf
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
https://github.com/avishai111/One-Class-Learning-with-Adaptive-Centroid-Shift-for-Audio-Deepfake-Detection
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
https://github.com/bastianwandt/elepose
https://github.com/beibuwandeluori/DFGC_Detection
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
https://github.com/berndpfrommer/basalt-headers
https://github.com/bev-gorry/underloc
https://github.com/bhiziroglu/3D-R2N2
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/bigaandsmallq/d3
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
https://github.com/blurgyy/jaxngp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/brack-wang/zeco
X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
https://github.com/bycloudai/instant-ngp-windows
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/c-sommer/gradient-sdf
Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction
https://github.com/cc-hpc-itwm/UpConv
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/cfernandezlab/CFL
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/charlesCXK/3D-SketchAware-SSC
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
https://github.com/charlesCXK/TorchSSC
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
https://github.com/charlesq34/pointnet-autoencoder
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
https://github.com/cheind/pure-torch-ngp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/chengguizi/basalt-headers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.