3 code implementations • CVPR 2016 • Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #27 on Visual Object Tracking on TrackingNet
no code implementations • 10 Jan 2016 • Anurag Arnab, Michael Sapienza, Stuart Golodetz, Julien Valentin, Ondrej Miksik, Shahram Izadi, Philip Torr
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
1 code implementation • 1 Dec 2016 • Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.
no code implementations • CVPR 2017 • Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip H. S. Torr, Patrick Pérez
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines.
1 code implementation • CVPR 2018 • Anurag Arnab, Ondrej Miksik, Philip H. S. Torr
Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation.
1 code implementation • 20 Feb 2018 • Oscar Rahnama, Duncan Frost, Ondrej Miksik, Philip H. S. Torr
For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts.
no code implementations • 17 Apr 2018 • Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip Torr
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
no code implementations • CONLL 2018 • Rory Beard, Ritwik Das, Raymond W. M. Ng, P. G. Keerthana Gopalakrishnan, Luka Eerens, Pawel Swietojanski, Ondrej Miksik
Natural human communication is nuanced and inherently multi-modal.
1 code implementation • 15 Mar 2019 • Ondrej Miksik, Vibhav Vineet
For each time step, our dynamic map maintains a relative pose of each volume with respect to the stationary background.
1 code implementation • ICCV 2021 • Mihai Dusmanu, Ondrej Miksik, Johannes L. Schönberger, Marc Pollefeys
Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems.
no code implementations • CVPR 2022 • Tien Do, Ondrej Miksik, Joseph DeGol, Hyun Soo Park, Sudipta N. Sinha
Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible.
1 code implementation • 11 Jul 2022 • Christopher Agia, Krishna Murthy Jatavallabhula, Mohamed Khodeir, Ondrej Miksik, Vibhav Vineet, Mustafa Mukadam, Liam Paull, Florian Shkurti
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
no code implementations • 22 Sep 2022 • Benoit Guillard, Sai Vemprala, Jayesh K. Gupta, Ondrej Miksik, Vibhav Vineet, Pascal Fua, Ashish Kapoor
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
no code implementations • 19 Oct 2022 • Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale, Lukas Gruber, Viktor Larsson, Ondrej Miksik, Marc Pollefeys
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
no code implementations • ICCV 2023 • Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (i. e., unknown overlap - if any - and changes in the environment).
1 code implementation • 28 Apr 2023 • Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (ie, unknown overlap -- if any -- and changes in the environment).
Ranked #1 on Point Cloud Registration on 3RScan
no code implementations • 15 Jun 2023 • Madeline Chantry Schiappa, Sachidanand VS, Yunhao Ge, Ondrej Miksik, Yogesh S. Rawat, Vibhav Vineet
Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of data using self-supervised or semi-supervised learning have emerged.