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DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars. (arXiv:2209.12729v2 [cs.CV] UPDATED)
Sept. 28, 2022, 1:15 a.m. | Florian Drews, Di Feng, Florian Faion, Lars Rosenbaum, Michael Ulrich, Claudius Gläser
cs.CV updates on arXiv.org arxiv.org
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 approach simple and flexible. Extracted features
are transformed into bird's-eye-view as a common representation for fusion.
Spatial and semantic alignment is performed prior to fusing modalities in the
feature space. Finally, a detection head exploits rich multi-modal features for
improved 3D detection performance. Experimental results …
More from arxiv.org / cs.CV updates on arXiv.org
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