About

The Monocular Depth Estimation is the task of estimating scene depth using a single image.

Source: Defocus Deblurring Using Dual-Pixel Data

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Greatest papers with code

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

High Quality Monocular Depth Estimation via Transfer Learning

31 Dec 2018ialhashim/DenseDepth

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

MONOCULAR DEPTH ESTIMATION TRANSFER LEARNING

GIMP-ML: Python Plugins for using Computer Vision Models in GIMP

27 Apr 2020kritiksoman/GIMP-ML

Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.

COLORIZATION DEBLURRING DENOISING IMAGE INPAINTING IMAGE MANIPULATION IMAGE SUPER-RESOLUTION MONOCULAR DEPTH ESTIMATION SEMANTIC SEGMENTATION SINGLE IMAGE DEHAZING VIDEO FRAME INTERPOLATION

Vision Transformers for Dense Prediction

24 Mar 2021intel-isl/MiDaS

We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks.

 Ranked #1 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)

MONOCULAR DEPTH ESTIMATION SEMANTIC SEGMENTATION

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

2 Jul 2019intel-isl/MiDaS

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.

MONOCULAR DEPTH ESTIMATION

Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

ICLR 2020 TRI-ML/packnet-sfm

Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions.

MONOCULAR DEPTH ESTIMATION REPRESENTATION LEARNING SELF-SUPERVISED LEARNING SEMANTIC SEGMENTATION

3D Packing for Self-Supervised Monocular Depth Estimation

CVPR 2020 TRI-ML/packnet-sfm

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.

MONOCULAR DEPTH ESTIMATION SELF-DRIVING CARS

FastDepth: Fast Monocular Depth Estimation on Embedded Systems

8 Mar 2019dwofk/fast-depth

In this paper, we address the problem of fast depth estimation on embedded systems.

MONOCULAR DEPTH ESTIMATION NETWORK PRUNING