Ziegler P, Mehl J, Franke J, Reitelshöfer S (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: SciTePress
Book Volume: 3
Pages Range: 529-537
Conference Proceedings Title: Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 3
Autonomous mobile systems (AMS) require robust environmental perception, particularly in challenging environments where individual sensors exhibit limitations due to their inherent physical measurement principles. Therefore, this paper examines different fusion strategies from various multimodal domains for the semantic segmentation task and introduces RIGATING, a novel mid-level fusion architecture that integrates sparse radar point clouds with RGB images for semantic segmentation. Using gated attention mechanisms, our RIGATING architecture combines dual encoders, DeepLabV3+ with ResNet-101 backbone for RGB and PointNet++ for radar feature extraction, fused at high and low levels to dynamically weigh complementary information, including radar cross-section and velocity. Evaluated on the Zenseact Open Dataset (ZOD), RIGATING matches the RGB-baseline in interference-free scenarios, while demonstrating superior robustness under perturbations such as noise, blur, brightness and full sen sor ablations, achieving over 70 percent IoU on radar inputs alone. These advancements enhance perception reliability for mobile systems in adverse conditions and lay the groundwork for more intelligent, human-like sensor data processing.
APA:
Ziegler, P., Mehl, J., Franke, J., & Reitelshöfer, S. (2026). RIGATING: Radar-Image Gated Attention Fusion for Multimodal Semantic Segmentation in Autonomous Mobile Systems. In Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 3 (pp. 529-537). Marbella, ES: SciTePress.
MLA:
Ziegler, Patrick, et al. "RIGATING: Radar-Image Gated Attention Fusion for Multimodal Semantic Segmentation in Autonomous Mobile Systems." Proceedings of the 21st International Conference on Computer Vision Theory and Applications (VISAPP), Marbella SciTePress, 2026. 529-537.
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