351 citations · 685 across the 23 of their papers we have counts for
9 papers · 1 filter
Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes
Yiming Dou, Wonseok Oh, Yuqing Luo +2
We study the problem of making 3D scene reconstructions interactive by asking the following question: can we predict the sounds of human hands physically interacting with a scene?…
Tactile-Augmented Radiance Fields
Yiming Dou, Fengyu Yang, Yi Liu +2
We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to…
Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano, Antonio Loquercio, Antoni Rosinol +2
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and somet…
Event-based Asynchronous Sparse Convolutional Networks
Nico Messikommer, Daniel Gehrig, Antonio Loquercio +1
Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such…
Learning Depth With Very Sparse Supervision
Antonio Loquercio, Alexey Dosovitskiy, Davide Scaramuzza
Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D pr…
A General Framework for Uncertainty Estimation in Deep Learning
Antonio Loquercio, Mattia Segù, Davide Scaramuzza
Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fund…