60 citations · 65 across the 5 of their papers we have counts for
15 papers · 1 filter
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Álvaro Parra, Shin-Fang Chng, Tat-Jun Chin +2
Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programmin…
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks
Dominic Jack, Frederic Maire, Simon Denman +1
Image convolutions have been a cornerstone of a great number of deep learning advances in computer vision. The research community is yet to settle on an equivalent operator for spa…
A Simple and Scalable Shape Representation for 3D Reconstruction
Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh +1
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN encoder-…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Implicitly Defined Layers in Neural Networks
Qianggong Zhang, Yanyang Gu, Michalkiewicz Mateusz +2
In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions. In this paper we demonstrate that defin…