Search Results for author: Chen Kong

Found 15 papers, 3 papers with code

FroDO: From Detections to 3D Objects

no code implementations11 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.

3D Reconstruction Object Reconstruction +1

Deep Non-Rigid Structure from Motion with Missing Data

no code implementations30 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.

Matrix Completion Structure from Motion

Deep Non-Rigid Structure from Motion

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.

Dictionary Learning Structure from Motion

Deep Interpretable Non-Rigid Structure from Motion

1 code implementation28 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.

Dictionary Learning Structure from Motion

CNNs are Globally Optimal Given Multi-Layer Support

no code implementations7 Dec 2017 Chen Huang, Chen Kong, Simon Lucey

Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs.

Take it in your stride: Do we need striding in CNNs?

no code implementations7 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.

Image2Mesh: A Learning Framework for Single Image 3D Reconstruction

1 code implementation29 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.

3D Reconstruction

Using Locally Corresponding CAD Models for Dense 3D Reconstructions From a Single Image

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.

Dictionary Learning Graph Embedding

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

3 code implementations21 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.

3D Object Reconstruction Point Cloud Generation

Generating Multi-Sentence Lingual Descriptions of Indoor Scenes

no code implementations28 Feb 2015 Dahua Lin, Chen Kong, Sanja Fidler, Raquel Urtasun

This paper proposes a novel framework for generating lingual descriptions of indoor scenes.

Text Generation

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