https://github.com/pingzhou-lf/light-field-depth-estimation-based-on-stitched-epis
공식 코드Light Field Depth Estimation via Stitched Epipolar Plane Images
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/pixie8888/mvsdet
공식 코드MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps
https://github.com/pku-yuangroup/holotime
공식 코드HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
https://github.com/poppinace/sapa
공식 코드SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
https://github.com/prgumd/MorphEyes
공식 코드MorphEyes: Variable Baseline Stereo For Quadrotor Navigation
https://github.com/princeton-computational-imaging/gated2gated
공식 코드Gated2Gated: Self-Supervised Depth Estimation from Gated Images
https://github.com/princeton-vl/DeepV2D
공식 코드DeepV2D: Video to Depth with Differentiable Structure from Motion
https://github.com/princeton-vl/raft-stereo
공식 코드RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
https://github.com/prismformore/InvPT
공식 코드InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
https://github.com/prismformore/multi-task-transformer
공식 코드Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
https://github.com/prs-eth/DeFlow
공식 코드https://github.com/prs-eth/marigold
공식 코드Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/prstrive/EPCDepth
공식 코드Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation
https://github.com/prstrive/unimvsnet
공식 코드Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
https://github.com/pufanqi23/monodgp
공식 코드MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors
https://github.com/pui-nantheera/DepthEstimation
공식 코드https://github.com/puzzlepaint/camera_calibration
공식 코드Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
https://github.com/qcraftai/gedepth
공식 코드https://github.com/qianyuzqy/iadg
공식 코드https://github.com/qinenergy/corda
공식 코드Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/raivnlab/mimic
공식 코드https://github.com/raja-kumar/depth-aware-3dgs
공식 코드Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting
https://github.com/rajeevpatwari/anynet
공식 코드Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques
https://github.com/raymondwang987/fmnet
공식 코드Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
https://github.com/raymondwang987/nvds
공식 코드NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
https://github.com/rbrandt1/MaxTreeS
공식 코드MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees
https://github.com/remadaher/shades
공식 코드SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition
https://github.com/revisitq/Pseudo-Stereo-3D
공식 코드Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
https://github.com/rickgroen/cov-weighting
공식 코드https://github.com/rickgroen/depthgan
공식 코드On the Benefit of Adversarial Training for Monocular Depth Estimation
https://github.com/ricupa/mtml-learn-how-to-adapt-to-unseen-tasks
공식 코드Multi-Task Meta Learning: learn how to adapt to unseen tasks
https://github.com/riiid/ppap
공식 코드https://github.com/rizhaocai/metapattern_fas
공식 코드https://github.com/rnlee1998/SRD
공식 코드https://github.com/robert-xiaoqiang/ds-net
공식 코드Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
https://github.com/robot-learning-freiburg/CoDEPS
공식 코드CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.