2 citations · 2 across the 6 of their papers we have counts for
6 papers
Variational Voxel Pseudo Image Tracking
Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis +2
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and si…
Uncertainty-Aware AB3DMOT by Variational 3D Object Detection
Illia Oleksiienko, Alexandros Iosifidis
Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world. Uncertainty estimation is an effective tool to provide statistically ac…
Layer Ensembles
Illia Oleksiienko, Alexandros Iosifidis
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and c…
Variational Neural Networks
Illia Oleksiienko, Dat Thanh Tran, Alexandros Iosifidis
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each inp…
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images
Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis +2
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo i…
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
Illia Oleksiienko, Alexandros Iosifidis
Real-time detection of objects in the 3D scene is one of the tasks an autonomous agent needs to perform for understanding its surroundings. While recent Deep Learning-based solutio…