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Found 6 papers, 3 papers with code
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
Ranked #6 on
Real-Time Semantic Segmentation
on NYU Depth v2
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks
At the time of writing, this is the first piece of work to showcase such a capability on a mobile platform.
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs
Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision.
Joint Pose and Principal Curvature Refinement Using Quadrics
In this paper we present a novel joint approach for optimising surface curvature and pose alignment.
A Fast Method For Computing Principal Curvatures From Range Images
In particular we compare our method to several alternatives to demonstrate the improvement.
Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image.
