Learning based methods have shown very promising results for the task of depth estimation in single images.
#7 best model for Monocular Depth Estimation on KITTI Eigen split
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
#5 best model for 3D Human Pose Estimation on Human3.6M
Per-pixel ground-truth depth data is challenging to acquire at scale.
#5 best model for Monocular Depth Estimation on KITTI Eigen split
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.