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Found 7 papers, 3 papers with code
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.
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
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 #5 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.
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.
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.
