https://github.com/jhonykaesemodel/image2mesh
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
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/jiangchaokang/3dsflabelling
$PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction
https://github.com/jiepengwang/NeuRIS
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
https://github.com/jmpu/deepfaketextdetection
https://github.com/joanrod/ocr-vqgan
https://github.com/joh-fischer/PlantLDM
https://github.com/josebeo2016/BTS-Encoder-ASVspoof
https://github.com/jtchen0528/PCL-I2G
https://github.com/judyye/ihoi
What's in your hands? 3D Reconstruction of Generic Objects in Hands
https://github.com/kair-bair/nerfacc
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/kaist-vclab/sphere-stereo
https://github.com/khalidw/MOT16_Annotator
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
https://github.com/komeiharada/learn_DL_together
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
https://github.com/kprav3/DeepFakeDetection_DIP
https://github.com/krrish94/ssc
https://github.com/kwea123/BlendedMVS_scenes
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
https://github.com/kwea123/CasMVSNet_pl
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
https://github.com/kwea123/MVSNet_pl
https://github.com/kwea123/ngp_pl
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/kysucix/gipuma
https://github.com/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/lilygoli/SpotLessSplats
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
https://github.com/liruihui/PU-GAN
https://github.com/liuf1990/Fully_3D_Object
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/liuzhengzhe/dreamstone-iss
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/lixucuhk/ASV-anti-spoofing-with-Res2Net
Replay and Synthetic Speech Detection with Res2net Architecture
https://github.com/lixucuhk/Channel-wise-Gated-Res2Net
Replay and Synthetic Speech Detection with Res2net Architecture
https://github.com/lukemelas/realfusion
RealFusion: 360° Reconstruction of Any Object from a Single Image
https://github.com/luo1cheng/lc2wf
https://github.com/lzx551402/geodesc
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
https://github.com/magicleap/Atlas
https://github.com/makslevental/openhls
https://github.com/maragori/DeepfakeForensics-v1
https://github.com/marian42/mesh_to_sdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/marian42/shapegan
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.