Depth Estimation
936 papers with code • 14 benchmarks • 75 datasets
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.
Benchmarks
These leaderboards are used to track progress in Depth Estimation
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Libraries
Use these libraries to find Depth Estimation models and implementations
Cityscapes
KITTI
ScanNet
NYUv2
Matterport3D
Middlebury
TUM RGB-D
SUNCG
2D-3D-S
Taskonomy
Subtasks
Most implemented papers
High Quality Monocular Depth Estimation via Transfer Learning
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Deeper Depth Prediction with Fully Convolutional Residual Networks
iro-cp/FCRN-DepthPrediction
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This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
Unsupervised Monocular Depth Estimation with Left-Right Consistency
mrharicot/monodepth
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CVPR 2017
Learning based methods have shown very promising results for the task of depth estimation in single images.
Digging Into Self-Supervised Monocular Depth Estimation
Per-pixel ground-truth depth data is challenging to acquire at scale.
Vision Transformers for Dense Prediction
isl-org/DPT
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ICCV 2021
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks.
Efficient Attention: Attention with Linear Complexities
cmsflash/efficient-attention
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Dot-product attention has wide applications in computer vision and natural language processing.
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
intel-isl/MiDaS
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In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks.
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
cogaplex-bts/bts
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We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
