https://github.com/zhijieshen-bjtu/neural-contourlet-network-for-mode
공식 코드Neural Contourlet Network for Monocular 360 Depth Estimation
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/zhijieshen-bjtu/panoformer
공식 코드PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation
https://github.com/zhoushen1/dcmpnet
공식 코드Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing
https://github.com/zhouzheyuan/sgm3d
공식 코드https://github.com/zhsun0357/seeing-far-in-the-dark-with-patterned-flash
공식 코드https://github.com/zhuofeng-wu/qpdnet
공식 코드https://github.com/zhwzhong/AHMF
공식 코드High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey
공식 코드https://github.com/zhwzhong/dagf
공식 코드https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/tree/main/configs/depthformer
공식 코드https://github.com/zhyever/PatchFusion
공식 코드https://github.com/zhyever/litedepth
공식 코드LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
https://github.com/zhyever/monocular-depth-estimation-toolbox
공식 코드BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation
https://github.com/zichengduan/mvchm
공식 코드https://github.com/zillow/psmnet-layout
공식 코드PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
https://github.com/ziongo6/hawkdrive
공식 코드HawkDrive: A Transformer-driven Visual Perception System for Autonomous Driving in Night Scene
https://github.com/ziqipang/addp
공식 코드https://github.com/zitongyu/flex-modal-fas
공식 코드https://github.com/ziweiwwang/shef
공식 코드https://github.com/zkawfanx/atlantis
공식 코드Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion
https://github.com/zm-zhou/tio-depth_pytorch
공식 코드Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
https://github.com/zqlin0521/cdgs-release
공식 코드CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting
https://github.com/zs670980918/arai-mvsnet
공식 코드https://github.com/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://github.com/ztsrxh/DVANet
공식 코드Depth-aware Volume Attention for Texture-less Stereo Matching
https://github.com/ztsrxh/roadbev
공식 코드https://github.com/zxcqlf/gasmono
공식 코드GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
https://github.com/zxcqlf/monovit
공식 코드MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
https://github.com/zxcqlf/robotcar_depthgt_generate
공식 코드Unsupervised Monocular Depth Estimation in Highly Complex Environments
https://github.com/zyangchen/mocha-stereo
공식 코드MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
https://github.com/zzangjinsun/NLSPN_ECCV20
공식 코드https://github.com/zzr-idam/4KDehazing
공식 코드4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images
https://github.com/zzzkkkyyy/padenet
공식 코드Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes
https://github.com/04RR/SAFENet-Pytorch-
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/AdaBin
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.