Depth Estimation
State-of-the-art leaderboards
Subtasks
Greatest papers with code
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
Unsupervised Monocular Depth Estimation with Left-Right Consistency
CVPR 2017
• mrharicot/monodepth
• 
Learning based methods have shown very promising results for the task of depth estimation in single images.
#7 best model for
Monocular Depth Estimation
on KITTI Eigen split
Deeper Depth Prediction with Fully Convolutional Residual Networks
• iro-cp/FCRN-DepthPrediction
•
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Pyramid Stereo Matching Network
CVPR 2018
• JiaRenChang/PSMNet
• 
The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume.
gvnn: Neural Network Library for Geometric Computer Vision
• ankurhanda/gvnn
• 
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
• DrSleep/light-weight-refinenet
•
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
DEPTH ESTIMATION REAL-TIME SEMANTIC SEGMENTATION SURFACE NORMALS ESTIMATION
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
ICCV 2017
• xingyizhou/pytorch-pose-hg-3d
•
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
#5 best model for
3D Human Pose Estimation
on Human3.6M
Digging Into Self-Supervised Monocular Depth Estimation
Per-pixel ground-truth depth data is challenging to acquire at scale.
#5 best model for
Monocular Depth Estimation
on KITTI Eigen split
IMAGE RECONSTRUCTION MONOCULAR DEPTH ESTIMATION SCENE UNDERSTANDING
Learning from Synthetic Humans
CVPR 2017
• gulvarol/surreal
•
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
DEPTH ESTIMATION HUMAN PART SEGMENTATION MOTION CAPTURE POSE ESTIMATION