https://github.com/kunkunlin1221/InstructFLIP
공식 코드InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/l-j-yuan/modest
공식 코드https://github.com/l1997i/DurLAR
공식 코드https://github.com/laiyingxin2/shield
공식 코드https://github.com/lastbasket/endo-4dgs
공식 코드Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
https://github.com/lavreniuk/evp
공식 코드https://github.com/lawy623/SVS
공식 코드https://github.com/layer6ai-labs/ssl-robustness
공식 코드Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
https://github.com/ldkong1205/robodepth
공식 코드The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
https://github.com/lehduong/onediffusion
공식 코드https://github.com/leiainc/holopix50k
공식 코드https://github.com/leisemann/ca_mmde
공식 코드Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective
https://github.com/leminhbinh0209/cvpr24-fas
공식 코드https://github.com/leungtsang/cpcdr
공식 코드Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation
https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth
공식 코드Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/lianqing11/monojsg
공식 코드MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
https://github.com/lijia7/sddr
공식 코드https://github.com/linlos1234/lfuie
공식 코드Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method
https://github.com/linshan-bin/occnerf
공식 코드OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments
https://github.com/liortalker/MindTheEdge
공식 코드Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation
https://github.com/liujf1226/mono-vifi
공식 코드https://github.com/liuyuml/depth-attention
공식 코드https://github.com/liuzechun/Bi-Real-net
공식 코드Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
https://github.com/lkyahpu/EPI_ORM
공식 코드EPI-based Oriented Relation Networks for Light Field Depth Estimation
https://github.com/lmb-freiburg/robustmvd
공식 코드A Benchmark and a Baseline for Robust Multi-view Depth Estimation
https://github.com/lmb-freiburg/sf2se3
공식 코드SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
https://github.com/lmomoy/cenet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/lmomoy/chnet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/lochenchou/DORN_radar
공식 코드Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network
https://github.com/lochenchou/rcdpt
공식 코드https://github.com/lofrienger/endodepthbenchmark
공식 코드Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data
https://github.com/logsanand/Monocular-depth-UAVimages
공식 코드Self-supervised monocular depth estimation from oblique UAV videos
https://github.com/lorafib/ref_depth
공식 코드Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering
https://github.com/lorenzopapa5/diffusion4d
공식 코드D4D: An RGBD diffusion model to boost monocular depth estimation
https://github.com/lorenzopapa5/meter
공식 코드METER: a mobile vision transformer architecture for monocular depth estimation
https://github.com/lpiccinelli-eth/UniK3D
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.