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Luigi Freda

Robotics & Computer Vision Engineer, PhD

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Machine LearningMathPosts

Optimization – why momentum really works

luigi 4 April 2017 0 Comments computer vision, machine-learning, math

This post explains why momentum works in optimization.  Nice interactive images are used to this aim.

Here you can find another overview of gradient-descent based methods.

 

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News About Me

  • 3DMR – 3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization
  • My new pySLAM v2 is out!
  • Member of the Program Committee of SAC 2020-IRMAS Track
  • PLVS for Circus Maximus Mixed-Reality Experience
  • Visual Perception and Spatial Computing

Blog

  • 3DMR – 3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization
  • My new pySLAM v2 is out!
  • ROSIntegration for Unreal Engine 4.23
  • PLVS for Circus Maximus Mixed-Reality Experience
  • Visual Perception and Spatial Computing

Tags

2D to 3D 3D reconstruction augmented reality business CNN computer vision data analysis dataset deep-learning disaster robotics drones energy features gps image processig inertial lidar machine-learning mapping math multi-robot NN open source perception place recognition robotics self-driving car sensor-based motion planning sensors SLAM TRADR UGVs USAR visual localization visual servoing VR

Tweets

Retweet on Twitter Luigi Freda Retweeted
holynski_ Aleksander Holynski @holynski_ ·
15 Sep

Check out our new paper that turns a (single image) => (interactive dynamic scene)!

I’ve had so much fun playing around with this demo.

Try it out yourself on the website: http://generative-dynamics.github.io/

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Retweet on Twitter Luigi Freda Retweeted
gkopanas Georgios Kopanas @gkopanas ·
4 Sep

The most important factor that determines the quality of a reconstruction is not the actual NeRF variant you are using but rather where you place the cameras.

We give insights on practical ways to solve this problem in realistic environments.

https://arxiv.org/abs/2309.00014

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