activity
20162021
most citedDeep Level Sets: Implicit Surface Representations for 3D Shape Inference

60 citations · 65 across the 4 of their papers we have counts for

collaborators

15 papers

cs.CV2021

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…

cs.CV2020

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…

cs.CV20205 cited

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-…

cs.CV2020

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…

cs.CV2020

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…

cs.CV2019

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Huu Le, Tuan Hoang, Qianggong Zhang +3

Visual localization has become a key enabling component of many place recognition and SLAM systems. Contemporary research has primarily focused on improving accuracy and precision-…