FCRN-DepthPrediction

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

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

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

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

Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching using pytorch-lightning

[ECCV 2018]: T2Net: Synthetic-to-Realistic Translation for Depth Estimation Tasks

The example of running Depth Prediction using Core ML

Code for T-ITS paper "Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry" and for ICRA paper "Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks".

TriDepth: Triangular Patch-based Deep Depth Prediction [Kaneko+, ICCVW2019(oral)]