1 code implementation • ICCV 2023 • Xiaqing Pan, Nicholas Charron, Yongqian Yang, Scott Peters, Thomas Whelan, Chen Kong, Omkar Parkhi, Richard Newcombe, Carl Yuheng Ren
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth.
no code implementations • 11 May 2020 • Kejie Li, Martin Rünz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.
no code implementations • ICCV 2019 • Chaoyang Wang, Chen Kong, Simon Lucey
This alleviates the data bottleneck, which is one of the major concern for supervised methods.
Ranked #19 on Weakly-supervised 3D Human Pose Estimation on Human3.6M
no code implementations • 30 Jul 2019 • Chen Kong, Simon Lucey
Non-Rigid Structure from Motion (NRSfM) refers to the problem of reconstructing cameras and the 3D point cloud of a non-rigid object from an ensemble of images with 2D correspondences.
no code implementations • ICCV 2019 • Chen Kong, Simon Lucey
Current non-rigid structure from motion (NRSfM) algorithms are mainly limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle.
1 code implementation • 28 Feb 2019 • Chen Kong, Simon Lucey
All current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle.
no code implementations • 7 Dec 2017 • Chen Kong, Simon Lucey
Since their inception, CNNs have utilized some type of striding operator to reduce the overlap of receptive fields and spatial dimensions.
no code implementations • 7 Dec 2017 • Chen Huang, Chen Kong, Simon Lucey
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs.
1 code implementation • 29 Nov 2017 • Jhony K. Pontes, Chen Kong, Sridha Sridharan, Simon Lucey, Anders Eriksson, Clinton Fookes
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks.
no code implementations • 23 Jul 2017 • Jhony K. Pontes, Chen Kong, Anders Eriksson, Clinton Fookes, Sridha Sridharan, Simon Lucey
3D reconstruction from 2D images is a central problem in computer vision.
no code implementations • CVPR 2017 • Chen Kong, Chen-Hsuan Lin, Simon Lucey
A common strategy in dictionary learning to encourage generalization is to allow for linear combinations of dictionary elements.
3 code implementations • 21 Jun 2017 • Chen-Hsuan Lin, Chen Kong, Simon Lucey
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
no code implementations • CVPR 2016 • Chen Kong, Simon Lucey
Many non-rigid 3D structures are not modelled well through a low-rank subspace assumption.
no code implementations • 28 Feb 2015 • Dahua Lin, Chen Kong, Sanja Fidler, Raquel Urtasun
This paper proposes a novel framework for generating lingual descriptions of indoor scenes.
no code implementations • CVPR 2014 • Chen Kong, Dahua Lin, Mohit Bansal, Raquel Urtasun, Sanja Fidler
In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing.
no code implementations • CVPR 2014 • Dahua Lin, Sanja Fidler, Chen Kong, Raquel Urtasun
In this paper, we tackle the problem of retrieving videos using complex natural language queries.