41 citations · 63 across the 12 of their papers we have counts for
12 papers
Detecting 3D Line Segments for 6DoF Pose Estimation with Limited Data
Matej Mok, Lukáš Gajdošech, Michal Mesároš +2
The task of 6DoF object pose estimation is one of the fundamental problems of 3D vision with many practical applications such as industrial automation. Traditional deep learning ap…
Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform
Matej Palider, Omar Eldardeer, Viktor Kocur
This paper evaluates the current gaze estimation methods within a human-robot interaction (HRI) context of a shared workspace scenario. We introduce a new, annotated dataset collec…
Efficient Vision-based Vehicle Speed Estimation
Andrej Macko, Lukáš Gajdošech, Viktor Kocur
This paper presents a computationally efficient method for vehicle speed estimation from traffic camera footage. Building upon previous work that utilizes 3D bounding boxes derived…
RePoseD: Efficient Relative Pose Estimation With Known Depth Information
Yaqing Ding, Viktor Kocur, Václav Vávra +4
Recent advances in monocular depth estimation methods (MDE) and their improved accuracy open new possibilities for their applications. In this paper, we investigate how monocular d…
On Representation of 3D Rotation in the Context of Deep Learning
Viktória Pravdová, Lukáš Gajdošech, Hassan Ali +1
This paper investigates various methods of representing 3D rotations and their impact on the learning process of deep neural networks. We evaluated the performance of ResNet18 netw…
Enhancement of 3D Camera Synthetic Training Data with Noise Models
Katarína Osvaldová, Lukáš Gajdošech, Viktor Kocur +1
The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and applying it on synthetic training data. We compi…
Robust Self-calibration of Focal Lengths from the Fundamental Matrix
Viktor Kocur, Daniel Kyselica, Zuzana Kukelova
The problem of self-calibration of two cameras from a given fundamental matrix is one of the basic problems in geometric computer vision. Under the assumption of known principal po…
Supersampling of Data from Structured-light Scanner with Deep Learning
Martin Melicherčík, Lukáš Gajdošech, Viktor Kocur +1
This paper focuses on increasing the resolution of depth maps obtained from 3D cameras using structured light technology. Two deep learning models FDSR and DKN are modified to work…
Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning
Tomáš Kunzo, Viktor Kocur, Lukáš Gajdošech +1
Teeth segmentation is an essential task in dental image analysis for accurate diagnosis and treatment planning. While supervised deep learning methods can be utilized for teeth seg…
Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans
Lukáš Gajdošech, Viktor Kocur, Martin Stuchlík +2
An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based method…
Traffic Camera Calibration via Vehicle Vanishing Point Detection
Viktor Kocur, Milan Ftáčnik
In this paper we propose a traffic surveillance camera calibration method based on detection of pairs of vanishing points associated with vehicles in the traffic surveillance foota…
Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement
Viktor Kocur, Milan Ftáčnik
Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorith…