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/guoyangzhao/fisheyedepth
공식 코드FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
https://github.com/gurayozgur/foundpad
공식 코드FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
https://github.com/hal-lucination/segfuse
공식 코드360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network
https://github.com/halleyjiang/plnet
공식 코드PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation
https://github.com/hamarh/HMNet_pth
공식 코드Hierarchical Neural Memory Network for Low Latency Event Processing
https://github.com/haofeixu/rdn4depth
공식 코드Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos
https://github.com/haomo-ai/modalink
공식 코드ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
https://github.com/haomo-ai/superfusion
공식 코드SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
https://github.com/haoqiwang/sinder
공식 코드https://github.com/happytianhao/tade
공식 코드Improving Bird's Eye View Semantic Segmentation by Task Decomposition
https://github.com/harborsarah/cafnet
공식 코드CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation
https://github.com/harborsarah/get-up
공식 코드GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling
https://github.com/harborsarah/lircdepth
공식 코드https://github.com/harboryuan/polyphonicformer
공식 코드PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
https://github.com/hekmak/Conf-net
공식 코드Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction
https://github.com/hht1996ok/ea-bev
공식 코드EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection
https://github.com/hitcslj/ssd
공식 코드Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
https://github.com/hkashyap/SparseMotion
공식 코드Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
https://github.com/hkust-vgd/lcd
공식 코드https://github.com/hmhemu/ra-depth
공식 코드RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
https://github.com/hoangchuongnguyen/mono-consistent-depth
공식 코드Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
https://github.com/hondamunigeprasannasilva/attack-attention
공식 코드Attacking Attention of Foundation Models Disrupts Downstream Tasks
https://github.com/hongklin/tide
공식 코드A Unified Image-Dense Annotation Generation Model for Underwater Scenes
https://github.com/hpc-lab-koreatech/tie-kd
공식 코드TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth Estimation
https://github.com/hs-yn/daps
공식 코드Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal Distillation
https://github.com/huang-yh/selfocc
공식 코드SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
https://github.com/hufu6371/DORN
공식 코드Deep Ordinal Regression Network for Monocular Depth Estimation
https://github.com/hustvl/sparsetrack
공식 코드SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
https://github.com/hyblue/fsre-depth
공식 코드https://github.com/hydroml/wtd
공식 코드Tackling water table depth modeling via machine learning: From proxy observations to verifiability
https://github.com/hyu-cvlab/omnimvs-pytorch
공식 코드OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
https://github.com/ialhashim/DenseDepth
공식 코드High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ian0926/gdnet
공식 코드Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
https://github.com/iccv2595/svdp
공식 코드Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
https://github.com/idiap/psfestimation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.