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3D geometry

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

https://github.com/simingyan/iae

공식 코드

Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning

https://github.com/siwensun/neural_diffeomorphic_flow--ndf

공식 코드

Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow

https://github.com/smiles724/3d-transformer

공식 코드

Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

https://github.com/smiles724/molformer

공식 코드

Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

https://github.com/sstzal/DFRF

공식 코드

Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis

https://github.com/stelzner/obsurf

공식 코드

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation

https://github.com/super-jw/LFADEA

공식 코드

Latent Feature and Attention Dual Erasure Attack against Multi-View Diffusion Models for 3D Assets Protection

https://github.com/takahikof/masklrf

공식 코드

MaskLRF: Self-supervised Pretraining via Masked Autoencoding of Local Reference Frames for Rotation-invariant 3D Point Set Analysis

https://github.com/tensorflow/models/tree/master/research/vid2depth

공식 코드

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

https://github.com/tgxs002/wikiscenes

공식 코드

Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision

https://github.com/thunguyenphuoc/BlockGAN

공식 코드

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

https://github.com/thunlp-mt/mean

공식 코드

Conditional Antibody Design as 3D Equivariant Graph Translation

https://github.com/tsattler/understanding_apr

공식 코드

Understanding the Limitations of CNN-based Absolute Camera Pose Regression

https://github.com/tum-vision/scenedino

공식 코드

Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion

https://github.com/ucaszyp/monoocc

공식 코드

MonoOcc: Digging into Monocular Semantic Occupancy Prediction

https://github.com/vagver/3dgazenet

공식 코드

3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views

https://github.com/vagver/dense3deyes

공식 코드

3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views

https://github.com/valeoai/gaussrender

공식 코드

GaussRender: Learning 3D Occupancy with Gaussian Rendering

https://github.com/vilab-ucsd/photoscene

공식 코드

PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes

https://github.com/viscom-ulm/GINN

공식 코드

Deep-learning the Latent Space of Light Transport

https://github.com/vitae-transformer/bevsimdet

공식 코드

SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection

https://github.com/vitae-transformer/simdistill

공식 코드

SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection

https://github.com/vkvats/GC-MVSNet-PlusPlus

공식 코드

Blending 3D Geometry and Machine Learning for Multi-View Stereopsis

https://github.com/wangzy22/xmask3d

공식 코드

XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation

https://github.com/wayveai/mile

공식 코드

Model-Based Imitation Learning for Urban Driving

https://github.com/wb014/FreeGS

공식 코드

Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding

https://github.com/whuwuteng/aerial_stereo_dataset

공식 코드

An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset

https://github.com/wjakubowska/neuralsurfacepriors

공식 코드

Neural Surface Priors for Editable Gaussian Splatting

https://github.com/wyysf-98/craftsman

공식 코드

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner

https://github.com/wyysf-98/craftsman3d

공식 코드

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner

https://github.com/wz7in/cvpr2023-vlsat

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VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud

https://github.com/xxlong0/ASNDepth

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Adaptive Surface Normal Constraint for Depth Estimation

https://github.com/xy-guo/LIGA-Stereo

공식 코드

LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

https://github.com/yangyanli/FPNN

공식 코드

FPNN: Field Probing Neural Networks for 3D Data

https://github.com/yanzq95/dhd

공식 코드

Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction

https://github.com/yifita/idf

공식 코드

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

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