108 citations · 839 across the 102 of their papers we have counts for
9 papers · 1 filter
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Jhony K. Pontes, Chen Kong, Sridha Sridharan +3
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representat…
Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM
Chanoh Park, Peyman Moghadam, Soohwan Kim +3
The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these adv…
Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories
Long Sha, Patrick Lucey, Stephan Zheng +3
We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared t…
Probabilistic Surfel Fusion for Dense LiDAR Mapping
Chanoh Park, Soohwan Kim, Peyman Moghadam +2
With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. Howev…
Compact Model Representation for 3D Reconstruction
Jhony K. Pontes, Chen Kong, Anders Eriksson +3
3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however t…
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes
Iman Abbasnejad, Sridha Sridharan, Simon Denman +2
In this paper the problem of complex event detection in the continuous domain (i.e. events with unknown starting and ending locations) is addressed. Existing event detection method…
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning a…
Tree Memory Networks for Modelling Long-term Temporal Dependencies
Tharindu Fernando, Simon Denman, Aaron McFadyen +2
In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question a…
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection
Tharindu Fernando, Simon Denman, Sridha Sridharan +1
As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including moni…