140 Open Source Depth Estimation Software Projects

[ICCV 2019] Monocular depth estimation from a single image

High Quality Monocular Depth Estimation via Transfer Learning

ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"

Unsupervised Scale-consistent Depth Learning from Video (IJCV2021 & NeurIPS 2019)

Unofficial implementation of Unsupervised Monocular Depth Estimation neural network MonoDepth in PyTorch

Convolutional Spatial Propagation Network

ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (Torch Implementation)

ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation)

🔥🔥🔥SLAM, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code

DEPRECATED: Depth Map Estimation from Monocular Images

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

Single Image Depth Estimation with Feature Pyramid Network

Learning Depth from Monocular Videos using Direct Methods, CVPR 2018

SGM，立体匹配StereoMatching最经典应用最广泛算法，4000+引用，兼顾效率和效果。完整实现，代码规范，注释清晰，博客教学，欢迎star！

The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at <http://www.cs.cmu.edu/~aayushb/pixelNet/>.

DeepTAM: Deep Tracking and Mapping https://lmb.informatik.uni-freiburg.de/people/zhouh/deeptam/

A heterogeneous and fully parallel stereo matching algorithm for depth estimation, implementing a local adaptive support weight (ADSW) Guided Image Filter (GIF) cost aggregation stage. Developed in both C++ and OpenCL.

Implementation of ICRA 2019 paper: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation

DSGN: Deep Stereo Geometry Network for 3D Object Detection (CVPR 2020)

Depth and Flow for Visual Odometry

List of projects for 3d reconstruction

FLaME: Fast Lightweight Mesh Estimation

[CVPR 2020] Estimation of the visible and hidden traversable space from a single color image

Multi-Task (Joint Segmentation / Depth / Surface Normas) Real-Time Light-Weight RefineNet

Code for Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, CVPR '19

[ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs

[ECCV 2020] Learning stereo from single images using monocular depth estimation networks

Dense Matching Benchmark

深度学习和三维视觉相关的论文

Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence, CVPR 2019

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

Tensorflow implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation

PatchMatchStereo，倾斜窗口经典，效果极佳，OpenMVS&Colmap稠密匹配算法。完整实现，代码规范，注释清晰，博客教学，欢迎star！

Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"

Geometry meets semantics for semi-supervised monocular depth estimation - ACCV 2018

Semi-supervised monocular depth map prediction

Estimating distance to objects in the scene using detection information

Robust realtime face and facial landmark tracking on CPU with Unity integration

Code for: "Learning To Adapt For Stereo" accepted at CVPR2019

The code and data of DiverseDepth

ROS bindings for FLaME: Fast Lightweight Mesh Estimation.

Keras code of my ICRA 2018 paper "Fast Disparity Estimation using Dense Networks"

Depth estimation with neural network, and learning on RGBD images

[CVPR2020] BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion

Official implementation of paper "Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields" (CVPR2020)

An easy-to-use wrapper for work with dense per-pixel tasks in PyTorch (including multi-task learning)

Tensorflow implementation(unofficial) of "Digging into Self-Supervised Monocular Depth Prediction"

:heavy_check_mark:A collection of code of depth estimation using light field data.

[ECCV'20] Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation

[CVPR 2020] Normal Assisted Stereo Depth Estimation

AD-Census立体匹配算法，中国学者Xing Mei等人研究成果（Respect！），算法效率高、效果出色，适合硬件加速，Intel RealSense D400 Stereo模块算法。完整实现，代码规范，注释清晰，欢迎star！

IROS'16/IJRR "Sparse Sensing for Resource-Constrained Depth Reconstruction"

Generalized Data-free Universal Adversarial Perturbations

Pytorch Implementation of Deeper Depth Prediction with Fully Convolutional Residual Networks

Multi-View Stereo by Temporal Nonparametric Fusion

Dual CNN Models for Unsupervised Monocular Depth Estimation

Probabilistic depth fusion based on Optimal Mixture of Gaussians for depth cameras

CVPR2018 - scene parsing network regulated by geometric prior

Multi-view stereo image-based 3D reconstruction

single and stereo calibration, disparity calculation.

simplified struct2depth in PyTorch

DIODE Development Toolkit

Matlab Implementation of a 3D Reconstruction algorithm

[ECCV 2020] Single image depth prediction allows us to rectify planar surfaces in images and extract view-invariant local features for better feature matching

Depth estimation from RGB images using fully convolutional neural networks.

MMDモーショントレース自動化一括処理バッチ

Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos, IJCAI 2019

This is the offical codes for the methods described in the "Feature-metric Loss for Self-supervised Learning of Depth and Egomotion".

Stereo depth estimation for self-driving cars 🚗

This is an implementation for our paper "Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching"

Refocus an image just by clicking on it with no additional data

Mono depth estimation for self-driving cars 🚗

A Pytorch implementation(unofficial) for paper "PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation"

This repository will provide tools and news about The OmniScape Dataset

Monocular depth estimation using Feature Pyramid Network implemented in PyTorch 1.1.0

Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding

Matlab code for 3D face reconstruction from stereo image pairs

Python ROS depth estimation from RGB image based on code from the paper "High Quality Monocular Depth Estimation via Transfer Learning"

Robust Depth Estimation for Light Field via Spinning Parallelogram Operator

Implementation of "Learning Across Tasks and Domains" ICCV 2019

Blu-ray 3D decoding, subtitle depth estimation and rendering tools for AviSynth

PyTorch implementation of Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

Unsupervised Monocular Depth Estimation with Left-Right Consistency by chainer

[ECCV 2020] Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance

FisheyeDistanceNet

Adversarial Structure Matching for Structured Prediction Tasks

Pytorch framework for supervised depth prediction

Unstructured multi-view depth estimation using mask-based multiplane representation

clothes research in 3D

[CVPR 2021] Self-supervised depth estimation from short sequences

Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)

[NeurIPS 2020] Code release for "Deep Multimodal Fusion by Channel Exchanging"

[CVPR 2021] Monocular depth estimation using wavelets for efficiency

Code and models for our ICCV 2021 paper "MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis"

Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)

Code for "DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion" (CVPR 2021)

Official Repo for the paper "Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods" (TMI)

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation