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Depth Estimation

78 papers with code · Computer Vision

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Greatest papers with code

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.

IMAGE RECONSTRUCTION MONOCULAR DEPTH ESTIMATION

Deeper Depth Prediction with Fully Convolutional Residual Networks

1 Jun 2016iro-cp/FCRN-DepthPrediction

This paper addresses the problem of estimating the depth map of a scene given a single RGB image.

DEPTH ESTIMATION

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

26 Mar 2018NVIDIA-AI-IOT/redtail

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.

AUTONOMOUS VEHICLES MONOCULAR DEPTH ESTIMATION

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.

DEPTH ESTIMATION STEREO MATCHING

gvnn: Neural Network Library for Geometric Computer Vision

25 Jul 2016ankurhanda/gvnn

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.

DEPTH ESTIMATION IMAGE RECONSTRUCTION VISUAL ODOMETRY

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations

13 Sep 2018DrSleep/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.

3D HUMAN POSE ESTIMATION DEPTH ESTIMATION TRANSFER LEARNING

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