https://github.com/addisonwang2013/dtl
공식 코드Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation
Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 14
- 보존된 평가 기록
- 58
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/adeandrade/research
공식 코드https://github.com/adonis-galaxy/depthclip
공식 코드https://github.com/adonis-galaxy/rsa
공식 코드RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
https://github.com/adonis-galaxy/wordepth
공식 코드WorDepth: Variational Language Prior for Monocular Depth Estimation
https://github.com/adriencourtois/sing
공식 코드https://github.com/adynathos/attentropy
공식 코드https://github.com/agneet42/robustness_depth_lang
공식 코드On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
https://github.com/ahmedheakl/fish-counting
공식 코드Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
https://github.com/ai-application-and-integration-lab/dgua_fas
공식 코드https://github.com/aim-uofa/AdelaiDepth
공식 코드https://github.com/aim-uofa/depth
공식 코드Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
https://github.com/aim-uofa/genpercept
공식 코드What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?
https://github.com/aim-uofa/geobench
공식 코드GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models
https://github.com/aimerykong/Recurrent-Scene-Parsing-with-Perspective-Understanding-in-the-loop
공식 코드Recurrent Scene Parsing with Perspective Understanding in the Loop
https://github.com/aipixel/gps-gaussian
공식 코드https://github.com/albert100121/AiFDepthNet
공식 코드Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision
https://github.com/alelopes/RGBD-Datasets
공식 코드https://github.com/alexklwong/adareg-monodispnet
공식 코드Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
https://github.com/alexklwong/targeted-adversarial-perturbations-monocular-depth
공식 코드Targeted Adversarial Perturbations for Monocular Depth Prediction
https://github.com/alexrich021/3dvnet
공식 코드3DVNet: Multi-View Depth Prediction and Volumetric Refinement
https://github.com/alextraveling/tomatoscanner
공식 코드TomatoScanner: phenotyping tomato fruit based on only RGB image
https://github.com/alibaba/UniFuse-Unidirectional-Fusion
공식 코드UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation
https://github.com/aliyun/NeWCRFs
공식 코드NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation
https://github.com/alpha-vllm/lumina-mgpt
공식 코드https://github.com/amirmael/semisupmde
공식 코드Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation
https://github.com/andrea-pilzer/unsup-stereo-depthGAN
공식 코드Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
https://github.com/andreaconti/ramdepth
공식 코드Range-Agnostic Multi-View Depth Estimation With Keyframe Selection
https://github.com/anger-man/unsupervised-depth-estimation
공식 코드Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
https://github.com/angie-lab-jhu/density_regression
공식 코드https://github.com/anitarau/domain-gap-reduction-endoscopy
공식 코드Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy
https://github.com/anitarau/simcol
공식 코드https://github.com/anjith2006/bob.paper.cross_modal_focal_loss_cvpr2021
공식 코드https://github.com/anjith2006/bob.paper.deep_pix_bis_pad.icb2019
공식 코드Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/anjith2006/bob.paper.ijcb2021_vision_transformer_pad
공식 코드On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
https://github.com/anqicheng1234/gam-depth
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.