Depth Prediction

129 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Deeper Depth Prediction with Fully Convolutional Residual Networks

iro-cp/FCRN-DepthPrediction 1 Jun 2016

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 CVPR 2017

Learning based methods have shown very promising results for the task of depth estimation in single images.

From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

cogaplex-bts/bts 24 Jul 2019

We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

fangchangma/sparse-to-dense 21 Sep 2017

We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.

Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras

google-research/google-research ICCV 2019

We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.

3D Ken Burns Effect from a Single Image

sniklaus/3d-ken-burns 12 Sep 2019

According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions.

Unsupervised Monocular Depth Learning in Dynamic Scenes

google-research/google-research 30 Oct 2020

We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture

yhlleo/DeepSegmentor ICCV 2015

In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.