pyslam v2.1 is available
I am happy to share the new version of pyslam: v2.1. It comes with new improvements and the following new
Read moreRobotics & Computer Vision Engineer, PhD
I am happy to share the new version of pyslam: v2.1. It comes with new improvements and the following new
Read moreI am excited to release pySLAM v2. The new version allows you to play with SLAM techniques, visual-odometry, keyframes, bundle-adjustment,
Read moreIn this very nice article, Jeff compares all the hottest deep learning frameworks that are used in the Academic and
Read moreFrom May 2018 I am Chief Research Officer at Inglobe Technologies. I will lead R&D activities on Computer Vision, Machine
Read moreAn Introduction to Machine Learning Below, you can find the original timetable of the course “Metodi Quantitativi per l’Informatica” (Introduction
Read moreIn this page, the Authors K. Tateno, F. Tombari, I. Laina and N. Navab present the following paper in which CNNs are used
Read moreIn this post, Torsten Sattler presents his upcoming CVPR 2017 paper “Comparative Evaluation of Hand-Crafted and Learned Local Features”
Read moreThis post explains why momentum works in optimization. Nice interactive images are used to this aim. Here you can find another
Read moreMicheal Milford explains how to make a driverless car see the road ahead. From his Google+ post: “We were asked to
Read moreThis post reports that a self-driving Uber car was involved in a high-speed crash in Tempe, Arizona. No one was
Read moreThis very nice post clearly explains the bias and variance tradeoff http://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/ This tradeoff can be mathematically stated by the
Read moreAccording to this post, Sebastian Thrun said “Uber has just bought a half-a-year-old company [Otto] with 70 employees for almost $700
Read moreA new journal paper about ElasticFusion has come out from Davison’s group. New exciting results on 3D light source mapping are presented.
Read moreThis post explains a trick which allows us to convert neural network outputs into probabilities, with no cost to performance,
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