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

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

Python scripts for performing stereo depth estimation using the HITNET Tensorflow model.

Python scripts performing stereo depth estimation using the CREStereo model in ONNX.

Code to extract stereo frame pairs from 3D videos, as used in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, arXiv:1907.01341"

MVSNet: Depth Inference for Unstructured Multi-view Stereo using pytorch-lightning

MultiViewStereoNet: Fast Multi-View Stereo Depth Estimation using Incremental Viewpoint-Compensated Feature Extraction

Python scripts form performing stereo depth estimation using the HITNET model in ONNX.

This repository provides a synchronized stereo matching pipeline using OAK cameras, generating RGB-D images with disparity-based depth and integrated IMU data. The output is fully compatible with RTAB-Map and Open3D for real-time 3D reconstruction, SLAM, and visual-inertial mapping applications.

Depth Estimation using Stereo images using deep learning based architecture for disparity measurement.The architectures used for disparity estimation are BgNet,CreStereo, Raft-Stereo, HitNet,GwcNet etc.

Stereo-image depth reconstruction with different matching costs and matching algorithms in Python using Numpy and Numba

Indoor/Outdoor SLAM using Stereo Vision. This project used ORB_SLAM2 with ZED stereo camera to achieve the task of SLAM and has a custom 2D occupancy grid mapping algorithm using depth.

Python scripts form performing stereo depth estimation using the high res stereo model in PyTorch .

Python scripts performing stereo depth estimation using the Fast-ACVNet model in ONNX.

Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX

实现了双目立体视觉标定(畸变与极线矫正)、深度图计算、获取像素点的空间坐标、三维点云显示。a simple demon of constructing a 3D pointcloud using stereo camera and it's depth map

Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

In this project, we try to implement the concept of Stereo Vision. We test the code on 3 different datasets, each of them contains 2 images of the same scenario but taken from two different camera angles. By comparing the information about a scene from 2 vantage points, we can obtain the 3D information by examining the relative positions of objects.

An introductory course on machine vision and related machine learning used in automation, autopilots, security and inspection systems. Topics covered include theory of computer and machine vision and related algorithms for image capture and processing, filtering, thresholds, edge detection, shape analysis, shape detection, salient object detection, pattern matching, digital image stabilization, stereo ranging, and methods of sensor and information fusion. Machine vision sensors covered include visible to long-wave infrared including passive EO/IR (Electro-Optical/Infrared) as well as active methods such as RGB depth mapping and LIDAR. Embedded and automation topics covered include implementation of these algorithms with FPGA or GP-GPU embedded real-time vision systems for autopilots (intelligent transportation), general machine vision automation and security including methods for detection, classification, recognition of targets, and applications including inspection, surveillance, search and rescue, and machine vision navigation.

Depth of field calculation based on binocular stereo matching using MATLAB

Programs to detect keyPoints in Images using SIFT, compute Homography and stitch images to create a Panorama and compute epilines and depth map between stereo images.

Stereo-Calib: Calibration, rectification, and depth estimation for stereo cameras using Charuco boards.

a ros node to publish depth from stereo block matching using the Realsense T265 Tracking Camera

Application for object detection with YOLOv4 and depth estimation using stereo cameras.

Depth reconstruction using stereo vision

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

Python scripts for generating synthetic stereo depth data using the UnrealCV library.

Developing a tool for the visually impaired people is not a recently emerged problem. But developing a computer aided tool is a still developing area. The aim of all these systems is to help the user in navigation without the help of a second person. There are several works using computer vision techniques. But there is no existing method that help to solve the all basic needs of blind person. This project presents a comprehensive scheme for reconstructing a three-dimensional (3D) model from a stereo camera via multi-view calibration. The depth information is useful so as to estimate the actual position of the target object. It is an essential parameter that can allow visualization from multiple perspectives. We provide an improved method for detecting objects and calculating the distance of these objects using stereo vision in real time. The stages involved include camera calibration, image rectifying, disparity calculation, and three dimensional reconstruction. Results show that objects located from 65cm up to 203cm are properly measured for their distances, with an average error of 3cm. The precision of the measurement also depends on the quality of the calibration.

This project uses MATLAB and python-based openCV to implement binocular ranging. Specifically, the camera is calibrated through stereo camera calibration in MATLAB, and then openCV is used for stereo correction, and then the disparity map is calculated on the corrected image to obtain Depth map. WLS filtering is used for optimization.

GPU Realtime depth passive stereo sensing using ADCensus