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of 56
pro vyhledávání: '"Kooij, Julian F. P."'
Autor:
Roldan, Ignacio, Palffy, Andras, Kooij, Julian F. P., Gavrila, Dariu M., Fioranelli, Francesco, Yarovoy, Alexander
The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchronized lidar data is used as ground truth to train a neural network with
Externí odkaz:
http://arxiv.org/abs/2406.04723
Given a ground-level query image and a geo-referenced aerial image that covers the query's local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused
Externí odkaz:
http://arxiv.org/abs/2406.00474
In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems, a feature extractor maps the query and reference imag
Externí odkaz:
http://arxiv.org/abs/2404.00546
Autor:
Roldan, Ignacio, Palffy, Andras, Kooij, Julian F. P., Gavrila, Dariu M., Fioranelli, Francesco, Yarovoy, Alexander
Publikováno v:
2024 IEEE Radar Conference (RadarConf24)
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-drive
Externí odkaz:
http://arxiv.org/abs/2402.12970
We introduce MuVieCAST, a modular multi-view consistent style transfer network architecture that enables consistent style transfer between multiple viewpoints of the same scene. This network architecture supports both sparse and dense views, making i
Externí odkaz:
http://arxiv.org/abs/2312.05046
Multi-sensor object detection is an active research topic in automated driving, but the robustness of such detection models against missing sensor input (modality missing), e.g., due to a sudden sensor failure, is a critical problem which remains und
Externí odkaz:
http://arxiv.org/abs/2309.14516
How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?
Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weig
Externí odkaz:
http://arxiv.org/abs/2305.05318
We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query's local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image
Externí odkaz:
http://arxiv.org/abs/2303.05915
This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature e
Externí odkaz:
http://arxiv.org/abs/2211.14651
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities. In this work, we investigate how aligning represen
Externí odkaz:
http://arxiv.org/abs/2211.13309