https://github.com/Max-Hermann/SelfSupervisedAerialDepthEstimator
공식 코드Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery
Papers with Code 아카이브
Depth Estimation
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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/MichaelRamamonjisoa/SharpNet
공식 코드SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation
https://github.com/MightyChaos/LKVOLearner
공식 코드https://github.com/MohitLamba94/L3Fnet
공식 코드Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging
https://github.com/NVIDIA-Jetson/redtail
공식 코드https://github.com/NVlabs/Bi3D
공식 코드https://github.com/NVlabs/FoundationStereo
공식 코드https://github.com/NVlabs/implicit_depth
공식 코드RGB-D Local Implicit Function for Depth Completion of Transparent Objects
https://github.com/NagabhushanSN95/ViP-NeRF
공식 코드ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
https://github.com/NeurAI-Lab/G2S
공식 코드Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
https://github.com/NeurAI-Lab/UniNet
공식 코드https://github.com/NiFangBaAGe/DATTT
공식 코드Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
https://github.com/Nightmare-n/DepthAnyVideo
공식 코드https://github.com/OIPLab-DUT/Deep-Light-Field-Depth-Estimation
공식 코드Learning Multi-modal Information for Robust Light Field Depth Estimation
https://github.com/Owen-Liuyuxuan/visualDet3D
공식 코드Ground-aware Monocular 3D Object Detection for Autonomous Driving
https://github.com/PJ-cs/DistanceEstimationTracking
공식 코드https://github.com/POSTECH-CVLab/InstaOrder
공식 코드https://github.com/PaddlePaddle/PaddleSeg
공식 코드https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/estimation/adds.md
공식 코드Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/Pei-KaiHuang/LDCformer_Ext
공식 코드Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing
https://github.com/QuanjianSong/LightMotion
공식 코드A Light and Tuning-free Method for Simulating Camera Motion in Video Generation
https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation
공식 코드Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
https://github.com/RuijieZhu94/EC-Depth
공식 코드https://github.com/RuijieZhu94/mmdepth/blob/main/projects/ScaleDepth/README.md
공식 코드ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation
https://github.com/RuijieZhu94/mmdepth/tree/main/projects/Plane2Depth
공식 코드Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth Estimation
https://github.com/Sangluisme/MultiviewLightEnhancedDepthSR
공식 코드https://github.com/SeasonDepth/SeasonDepth
공식 코드https://github.com/SeokjuLee/Insta-DM
공식 코드Instance-wise Depth and Motion Learning from Monocular Videos
https://github.com/Shreeyak/cleargrasp
공식 코드ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
https://github.com/ShuweiShao/NDDepth
공식 코드NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
https://github.com/SibylGao/MSCVP-MVSNet
공식 코드Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
https://github.com/SwinTransformer/MIM-Depth-Estimation
공식 코드https://github.com/SysCV/idisc
공식 코드iDisc: Internal Discretization for Monocular Depth Estimation
https://github.com/TJ-IPLab/DNet
공식 코드https://github.com/TRAILab/CaDDN
공식 코드Categorical Depth Distribution Network for Monocular 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.