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demon

DeMoN: Depth and Motion Network

densedepth

High Quality Monocular Depth Estimation via Transfer Learning

densedescriptorlearning-pytorch

Pytorch implementation of paper named "Extremely Dense Point Correspondences using a Learned Feature Descriptor"

densenet-tensorflow

Reimplementation of DenseNet

depth-hints

Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs

depth-map-prediction

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

depth-refinement-and-normal-estimation

A graph-based framework to refine a noisy and possibly incomplete depth map and estimate the corresponding normal map.

depth-vo-feat

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

depth2normal

depth2pointcloud

Based on pcl,the program transformes the rgb image and depth image to the corresponding pointcloud.

detr

End-to-End Object Detection with Transformers

df-vo

Depth and Flow for Visual Odometry

dlf-slam

deep cnn feature slam based on ORB-SLAM

dorn

dorn_depth_estimation

Deep Ordinal Regression Network for Monocular Depth Estimation.（在PyTorch上实现CVPR2018论文，深度估计排名第一）

draw_convnet

drone_visual_slam

This project focuses on a fusion of monocular vision and IMU to robustly track the position of an AR drone using LSD-SLAM (Large-Scale Direct Monocular SLAM) algorithm.

ds-slam

dso

dso个人注释版

dsodemo

DSO SLAM demo for Android

dsr

Depth Super-Resolution based on Deep Edge-Aware Learning

dual-cnn-models-for-unsupervised-monocular-depth-estimation

Dual CNN Models for Unsupervised Monocular Depth Estimation

dynamic_orb_slam2

Visual SLAM system that can identify and exclude dynamic objects.

dynaslam

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups

dynslam

Master's Thesis on Simultaneous Localization and Mapping in dynamic environments. Separately reconstructs both the static environment and the dynamic objects from it, such as cars.

eecs442-challenge-surface-normal-estimation

Estimate surface normals from a single image; 3-stack-hourglass Architecture using Tensorflow; Reduce the Mean Angle Error down to 0.4397164234

efficientnet-pytorch

A PyTorch implementation of EfficientNet

efficientps

PyTorch code for training EfficientPS for Panoptic Segmentation https://rl.uni-freiburg.de/research/panoptic

esrgan

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution (Third Region)

f1tenth_simulator

Simulator for the f1/10th Autonomous Cars.