Search Results for author:
Found 7 papers, 3 papers with code
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.
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image.
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.
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.
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
Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change.
Robotics
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points
Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy.
