Search Results for author: Ondrej Miksik

Found 14 papers, 7 papers with code

LaMAR: Benchmarking Localization and Mapping for Augmented Reality

no code implementations19 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.

Learning to Simulate Realistic LiDARs

no code implementations22 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.

Learning To Detect Scene Landmarks for Camera Localization

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.

Camera Localization Image Retrieval +2

Cross-Descriptor Visual Localization and Mapping

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.

Mixed Reality Visual Localization

Live Reconstruction of Large-Scale Dynamic Outdoor Worlds

1 code implementation15 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.

3D Reconstruction Pose Estimation

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

1 code implementation20 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.

Stereo Matching Stereo Matching Hand

On the Robustness of Semantic Segmentation Models to Adversarial Attacks

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.

General Classification Image Classification +2

ROAM: a Rich Object Appearance Model with Application to Rotoscoping

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.

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

1 code implementation1 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.

object-detection Object Detection +3

Joint Object-Material Category Segmentation from Audio-Visual Cues

no code implementations10 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.

Staple: Complementary Learners for Real-Time Tracking

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.

regression Visual Object Tracking

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