Papers with Code 아카이브

3D geometry

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Shiaoming/vid2depth

Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints

https://github.com/Tom-Zheng/depth_single_image

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

https://github.com/Wapity/Pixel2Mesh-Pytorch

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

https://github.com/a-imamshah/savi-pytorch

Conditional Object-Centric Learning from Video

https://github.com/abefetterman/hamstir-gym

CAD2RL: Real Single-Image Flight without a Single Real Image

https://github.com/akar43/lsm

Learning a Multi-View Stereo Machine

https://github.com/allenai/object-edit

OBJECT 3DIT: Language-guided 3D-aware Image Editing

https://github.com/aluo-x/learning_neural_acoustic_fields

AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis

https://github.com/angeladai/cnncomplete

Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis

https://github.com/bruinxiong/EG3D-pytorch

Efficient Geometry-aware 3D Generative Adversarial Networks

https://github.com/c-sommer/gradient-sdf

Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction

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/code-implementation1/Code1/tree/main/DepthNet

S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

https://github.com/crazyleg/workshop-3d-neural

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

https://github.com/cure-lab/magicdrive

MagicDrive: Street View Generation with Diverse 3D Geometry Control

https://github.com/cvlab-columbia/zero123

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors

https://github.com/devlearning-gt/3DShapeGen

Occupancy Networks: Learning 3D Reconstruction in Function Space

https://github.com/dsshim0125/dacl

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

https://github.com/eowjd0512/VITAMIN-E

VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points

https://github.com/even-jk/peft-3d

Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding

https://github.com/fuxiao0719/GeoWizard

GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image

https://github.com/germain-hug/NRE

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

https://github.com/guochengqian/magic123

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

https://github.com/happytianhao/tade

NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection

https://github.com/hjwdzh/FrameNet

FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image

https://github.com/hlinchen/gnesf

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

https://github.com/hongsukchoi/handnerf_release

MonoNHR: Monocular Neural Human Renderer

https://github.com/hrhodin/NeuralSceneDecomposition

Neural Scene Decomposition for Multi-Person Motion Capture

https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning

Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

https://github.com/hsp-iit/byogg

Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping

https://github.com/imran3180/depth-map-prediction

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

https://github.com/ivan-tang-3d/peft-3d

Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding

https://github.com/ivan-tang-3d/point-peft

Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding

https://github.com/ivan-tang-3d/viewrefer3d

Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding

https://github.com/jchibane/if-net_texture

Implicit Feature Networks for Texture Completion from Partial 3D Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.