Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Faion, Florian"'
Group regression is commonly used in 3D object detection to predict box parameters of similar classes in a joint head, aiming to benefit from similarities while separating highly dissimilar classes. For query-based perception methods, this has, so fa
Externí odkaz:
http://arxiv.org/abs/2308.14481
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention weights are computed in each forward pass, giving insights into the mo
Externí odkaz:
http://arxiv.org/abs/2210.14391
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field of research, it has proved difficult to apply such models to point clou
Externí odkaz:
http://arxiv.org/abs/2209.15258
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection. Specialized feature extractors take advantage of each modality and can be exchanged easily, making the app
Externí odkaz:
http://arxiv.org/abs/2209.12729
Autor:
Niederlöhner, Daniel, Ulrich, Michael, Braun, Sascha, Köhler, Daniel, Faion, Florian, Gläser, Claudius, Treptow, André, Blume, Holger
Publikováno v:
2022 IEEE Intelligent Vehicles Symposium (IV), 04-09 June 2022, Aachen Germany, pp. 352-359
This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms of generating its own training signal for the velocities. Labels are o
Externí odkaz:
http://arxiv.org/abs/2207.03146
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as othe
Externí odkaz:
http://arxiv.org/abs/2205.15730
Autor:
Ulrich, Michael, Braun, Sascha, Köhler, Daniel, Niederlöhner, Daniel, Faion, Florian, Gläser, Claudius, Blume, Holger
This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bir
Externí odkaz:
http://arxiv.org/abs/2205.02111
Autor:
Richter, Jasmine, Faion, Florian, Feng, Di, Becker, Paul Benedikt, Sielecki, Piotr, Glaeser, Claudius
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate training datasets
Externí odkaz:
http://arxiv.org/abs/2204.10024
We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object. The key idea of the presented method is to represent the shape of the object using spherical harmonics, similar to th
Externí odkaz:
http://arxiv.org/abs/2012.13580
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