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/cvlab-kaist/PF3plat
공식 코드https://github.com/cvlab-kaist/seurat
공식 코드https://github.com/cvlab-unibo/d4-dbst
공식 코드https://github.com/dabbrata/depth-estimation-enc-dec
공식 코드Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation
https://github.com/daiszh/A-TVSNet
공식 코드A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation
https://github.com/damaggu/tadp
공식 코드https://github.com/daniel-xsy/bev-attack
공식 코드On the Adversarial Robustness of Camera-based 3D Object Detection
https://github.com/danielming123/inversematrixvt3d
공식 코드InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
https://github.com/danxuhk/ContinuousCRF-CNN
공식 코드Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
https://github.com/danxuhk/StructuredAttentionDepthEstimation
공식 코드Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
https://github.com/davidmhart/SelectionConv
공식 코드SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data
https://github.com/davidyan2001/synthetic2real-depth
공식 코드https://github.com/ddz16/UWEnhancer
공식 코드A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
https://github.com/deebuls/uncertainty-robustness-deep-regression-benchmark
공식 코드Maximum Likelihood Uncertainty Estimation: Robustness to Outliers
https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/deepmind/lab
공식 코드https://github.com/deepmind/ssl_hsic
공식 코드Self-Supervised Learning with Kernel Dependence Maximization
https://github.com/densoitlab/hcformer
공식 코드Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks
https://github.com/denyingmxd/CVCDepth
공식 코드Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation
https://github.com/denyingmxd/selftof
공식 코드Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular Images
https://github.com/divamgupta/sbevnet-stereo-layout-estimation
공식 코드https://github.com/dive128/dmvsnet
공식 코드https://github.com/dluvizon/3d-pose-consensus
공식 코드Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinates
https://github.com/doihye/Adaptive-confidence-thresholding
공식 코드Adaptive confidence thresholding for monocular depth estimation
https://github.com/donglao/depthtosemantic
공식 코드On the Viability of Monocular Depth Pre-training for Semantic Segmentation
https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
https://github.com/donkeymouse/thermalchameleon
공식 코드Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
https://github.com/dreamscene4d/dreamscene4d
공식 코드DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
https://github.com/drilistbox/3dppe
공식 코드3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers
https://github.com/drsleep/nas-segm-pytorch
공식 코드Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/dsshim0125/SwinDepth
공식 코드https://github.com/dsshim0125/grmc
공식 코드https://github.com/dtc111111/neslam
공식 코드https://github.com/duanyiqun/diffusiondepth
공식 코드DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation
https://github.com/dulucas/Displacement_Field
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.