Depth Estimation

441 papers with code • 10 benchmarks • 55 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.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

Libraries

Use these libraries to find Depth Estimation models and implementations

Most implemented papers

High Quality Monocular Depth Estimation via Transfer Learning

ialhashim/DenseDepth 31 Dec 2018

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.

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.

Digging Into Self-Supervised Monocular Depth Estimation

nianticlabs/monodepth2 4 Jun 2018

Per-pixel ground-truth depth data is challenging to acquire at scale.

Vision Transformers for Dense Prediction

isl-org/DPT 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 4 Dec 2018

Dot-product attention has wide applications in computer vision and natural language processing.

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

kyle-dorman/bayesian-neural-network-blogpost NeurIPS 2017

On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data.

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

intel-isl/MiDaS 2 Jul 2019

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 24 Jul 2019

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