2 code implementations • 12 Feb 2023 • Illia Oleksiienko, Alexandros Iosifidis
Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world.
2 code implementations • 12 Feb 2023 • Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis, Anastasios Tefas, Alexandros Iosifidis
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor.
2 code implementations • 10 Oct 2022 • 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 computing the difference in those predictions.
3 code implementations • 4 Jul 2022 • 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 input.
2 code implementations • 6 Jun 2022 • Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis, Anastasios Tefas, Alexandros Iosifidis
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT).
1 code implementation • 21 May 2021 • Illia Oleksiienko, Alexandros Iosifidis
This means that the methods can achieve a speed-up of $40$-$60\%$ by restricting operation to near objects while not sacrificing much in performance.