https://github.com/bravegroup/dcd
공식 코드Densely Constrained Depth Estimator for Monocular 3D Object Detection
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/brownvc/diffdiffdepth
공식 코드Differentiable Diffusion for Dense Depth Estimation from Multi-view Images
https://github.com/brownvc/lightfielddepth
공식 코드https://github.com/cake-lab/Mobile-AR-Depth-Estimation
공식 코드Mobile AR Depth Estimation: Challenges & Prospects -- Extended Version
https://github.com/cake-lab/hybriddepth
공식 코드HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors
https://github.com/canessae/altiro3D
공식 코드altiro3D: Scene representation from single image and novel view synthesis
https://github.com/caodinhduc/x-pdnet-official
공식 코드https://github.com/ccc870206/3d-pl
공식 코드3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
https://github.com/cea-list/monoprob
공식 코드MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
https://github.com/cfzd/monoground
공식 코드https://github.com/chaochao-lin/atr-fas
공식 코드Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
https://github.com/chaowentao/occcasnet
공식 코드OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
https://github.com/chaowentao/subfocal
공식 코드Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation
https://github.com/chelsea234/multi-domain-learning-fas
공식 코드Multi-domain Learning for Updating Face Anti-spoofing Models
https://github.com/chen742/DCF
공식 코드Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chenhoy/droid-splat
공식 코드DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
https://github.com/chenqikong/m3fas
공식 코드M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
https://github.com/chenxuluo/EPC
공식 코드Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
https://github.com/choyingw/SCADC-DepthCompletion
공식 코드Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
https://github.com/clks-wzz/FAS-SGTD
공식 코드Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/clks-wzz/epcr
공식 코드https://github.com/cmsflash/efficient-attention
공식 코드https://github.com/cnexah/va-depthnet
공식 코드VA-DepthNet: A Variational Approach to Single Image Depth Prediction
https://github.com/cogaplex-bts/bts
공식 코드From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
https://github.com/cogsys-tuebingen/mobilestereonet
공식 코드MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
https://github.com/cogsys-tuebingen/tristereonet
공식 코드TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation
https://github.com/complight/autocolor
공식 코드AutoColor: Learned Light Power Control for Multi-Color Holograms
https://github.com/compphoto/BoostingMonocularDepth
공식 코드https://github.com/compphoto/SIDepth
공식 코드https://github.com/comsee-research/blade
공식 코드Blur aware metric depth estimation with multi-focus plenoptic cameras
https://github.com/comsee-research/libpleno
공식 코드Blur aware metric depth estimation with multi-focus plenoptic cameras
https://github.com/comsee-research/plenoptic-datasets
공식 코드Blur aware metric depth estimation with multi-focus plenoptic cameras
https://github.com/csiplab/programmable3dcam
공식 코드A Simple Framework for 3D Lensless Imaging with Programmable Masks
https://github.com/cubhe/precise-point-spread-function-estimation
공식 코드https://github.com/cv-rits/densemtl
공식 코드DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
https://github.com/cv-stuttgart/sceneflow_from_blender
공식 코드Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.