Search Results for author: Aron Monszpart

Found 11 papers, 4 papers with code

Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision

1 code implementation6 Apr 2021 Mehmet Ozgur Turkoglu, Eric Brachmann, Konrad Schindler, Gabriel Brostow, Aron Monszpart

Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.

regression

Footprints and Free Space from a Single Color Image

1 code implementation CVPR 2020 Jamie Watson, Michael Firman, Aron Monszpart, Gabriel J. Brostow

We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input.

Semantic Segmentation

Unsupervised Intuitive Physics from Past Experiences

no code implementations26 May 2019 Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi

We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.

Meta-Learning

iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos

no code implementations20 Jun 2018 Aron Monszpart, Paul Guerrero, Duygu Ceylan, Ersin Yumer, Niloy J. Mitra

A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.

Human-Object Interaction Detection Object

Unsupervised Intuitive Physics from Visual Observations

no code implementations14 May 2018 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times.

Taking Visual Motion Prediction To New Heightfields

no code implementations22 Dec 2017 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data.

motion prediction

Learning to Represent Mechanics via Long-term Extrapolation and Interpolation

no code implementations6 Jun 2017 Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters.

Learning A Physical Long-term Predictor

no code implementations1 Mar 2017 Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi

Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena.

SMASH: Physics-guided Reconstruction of Collisions from Videos

1 code implementation29 Mar 2016 Aron Monszpart, Nils Thuerey, Niloy J. Mitra

Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized.

3D Reconstruction valid

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