Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Del Pero, Luca"'
Autor:
Platinsky, Lukas, Naseer, Tayyab, Chen, Hui, Haines, Ben, Zhu, Haoyue, Grimmett, Hugo, Del Pero, Luca
With the Autonomous Vehicle (AV) industry shifting towards machine-learned approaches for motion planning, the performance of self-driving systems is starting to rely heavily on large quantities of expert driving demonstrations. However, collecting t
Externí odkaz:
http://arxiv.org/abs/2203.01681
Despite the numerous successes of machine learning over the past decade (image recognition, decision-making, NLP, image synthesis), self-driving technology has not yet followed the same trend. In this paper, we study the history, composition, and dev
Externí odkaz:
http://arxiv.org/abs/2107.08142
Autor:
Bergamini, Luca, Ye, Yawei, Scheel, Oliver, Chen, Long, Hu, Chih, Del Pero, Luca, Osinski, Blazej, Grimmett, Hugo, Ondruska, Peter
In this work, we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of self-driving system performance without relying on expensive and time-con
Externí odkaz:
http://arxiv.org/abs/2105.12332
Autor:
Chen, Long, Platinsky, Lukas, Speichert, Stefanie, Osinski, Blazej, Scheel, Oliver, Ye, Yawei, Grimmett, Hugo, del Pero, Luca, Ondruska, Peter
We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners are very hungry for training data, which is usually collected using vehicles equipped wit
Externí odkaz:
http://arxiv.org/abs/2105.12337
We propose a new method for estimating the relative pose between two images, where we jointly learn keypoint detection, description extraction, matching and robust pose estimation. While our architecture follows the traditional pipeline for pose esti
Externí odkaz:
http://arxiv.org/abs/2104.01085
Autor:
Platinsky, Lukas, Szabados, Michal, Hlasek, Filip, Hemsley, Ross, Del Pero, Luca, Pancik, Andrej, Baum, Bryan, Grimmett, Hugo, Ondruska, Peter
In this paper we present the first published end-to-end production computer-vision system for powering city-scale shared augmented reality experiences on mobile devices. In doing so we propose a new formulation for an experience-based mapping framewo
Externí odkaz:
http://arxiv.org/abs/2011.05370
Autor:
Del Pero, Luca
People can understand the content of an image without effort. We can easily identify the objects in it, and figure out where they are in the 3D world. Automating these abilities is critical for many applications, like robotics, autonomous driving and
Externí odkaz:
http://hdl.handle.net/10150/297040
Publikováno v:
International Journal of Computer Vision (IJCV), July 2016
We propose an automatic system for organizing the content of a collection of unstructured videos of an articulated object class (e.g. tiger, horse). By exploiting the recurring motion patterns of the class across videos, our system: 1) identifies its
Externí odkaz:
http://arxiv.org/abs/1511.09319
Given unstructured videos of deformable objects, we automatically recover spatiotemporal correspondences to map one object to another (such as animals in the wild). While traditional methods based on appearance fail in such challenging conditions, we
Externí odkaz:
http://arxiv.org/abs/1412.0477
We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers in the wild. We discover consistent patterns in a bottom-up manner by
Externí odkaz:
http://arxiv.org/abs/1411.7883