About

Depth Estimation is a crucial step towards inferring scene geometry from 2D images. The goal in monocular Depth Estimation is to predict the depth value of each pixel, given only a single RGB image as input.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

Benchmarks

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Subtasks

Datasets

Greatest papers with code

Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints

CVPR 2018 tensorflow/models

We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.

DEPTH AND CAMERA MOTION

Unsupervised Monocular Depth Learning in Dynamic Scenes

30 Oct 2020google-research/google-research

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.

DEPTH ESTIMATION

Learning Single Camera Depth Estimation using Dual-Pixels

ICCV 2019 google-research/google-research

Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth.

MONOCULAR DEPTH ESTIMATION

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

ICCV 2019 google-research/google-research

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.

DEPTH ESTIMATION

A General and Adaptive Robust Loss Function

CVPR 2019 google-research/google-research

We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions.

IMAGE GENERATION

Learning to Navigate in Complex Environments

11 Nov 2016deepmind/lab

Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.

DEPTH ESTIMATION

Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge

29 Sep 20161adrianb/face-alignment

Our method builds upon the idea of convolutional part heatmap regression [1], extending it for 3D face alignment.

DEPTH ESTIMATION FACE ALIGNMENT