Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/TRI-ML/KP3D
공식 코드Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
https://github.com/TRI-ML/packnet-sfm
공식 코드https://github.com/TRI-ML/vidar
공식 코드https://github.com/TWJianNuo/EdgeDepth-Release
공식 코드The Edge of Depth: Explicit Constraints between Segmentation and Depth
https://github.com/Tencent/DepthCrafter
공식 코드DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
https://github.com/ToyotaResearchInstitute/packnet-sfm
공식 코드https://github.com/Tsinghua-MARS-Lab/CVT-Occ
공식 코드CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction
https://github.com/UCI-ISA-Lab/MultiHeadDepth-HomoDepth
공식 코드Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses
https://github.com/Uehwan/rmd
공식 코드https://github.com/Usman1021/DLE
공식 코드Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
https://github.com/VCL3D/Pano3D
공식 코드Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation
https://github.com/Visual-Behavior/GroCo
공식 코드GroCo: Ground Constraint for Metric Self-Supervised Monocular Depth
https://github.com/VisualComputingInstitute/diffusion-e2e-ft
공식 코드Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
https://github.com/VityaVitalich/MultiModalCoreset
공식 코드Challenges of Multi-Modal Coreset Selection for Depth Prediction
https://github.com/WXinlong/ForeSeE
공식 코드Task-Aware Monocular Depth Estimation for 3D Object Detection
https://github.com/Wenchao-Du/GAENet
공식 코드https://github.com/Westlake-AGI-Lab/Distill-Any-Depth
공식 코드Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator
https://github.com/WindQingYang/PipeNet
공식 코드PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
https://github.com/XinJCheng/CSPN
공식 코드Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
https://github.com/Xt-Chen/SARPN
공식 코드Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
https://github.com/XuefengBUPT/BS-Net
공식 코드Boundary-induced and scene-aggregated network for monocular depth prediction
https://github.com/XuelianCheng/LEAStereo
공식 코드Hierarchical Neural Architecture Search for Deep Stereo Matching
https://github.com/YinminZhang/MonoGeo
공식 코드Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection
https://github.com/ZM-Zhou/OCFD-Net_pytorch
공식 코드Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation
https://github.com/ZM-Zhou/SDFA-Net_pytorch
공식 코드Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation
https://github.com/ZM-Zhou/SMDE-Pytorch
공식 코드Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation
https://github.com/ZamDimon/liveness-detection
공식 코드Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis
https://github.com/Zengyi-Qin/MonoGRNet
공식 코드MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
https://github.com/ZitongYu/CDCN
공식 코드Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/ZitongYu/DeepFAS
공식 코드https://github.com/a-jahani/semiDepth
공식 코드Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network
https://github.com/a1600012888/mutr3d
공식 코드MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
https://github.com/aasensio/deepvel
공식 코드DeepVel: deep learning for the estimation of horizontal velocities at the solar surface
https://github.com/aasharma90/NighttimeDepthandFlow
공식 코드https://github.com/abrain-one/anyu
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.