15 citations · 18 across the 26 of their papers we have counts for
8 papers · 1 filter
LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance
Huawei Sun, Nastassia Vysotskaya, Tobias Sukianto +5
Recently, radar-camera fusion algorithms have gained significant attention as radar sensors provide geometric information that complements the limitations of cameras. However, most…
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli +2
The increasing demand in artificial intelligence (AI) for models that are both effective and explainable is critical in domains where safety and trust are paramount. In this study,…
GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling
Huawei Sun, Zixu Wang, Hao Feng +3
Depth estimation plays a pivotal role in autonomous driving, facilitating a comprehensive understanding of the vehicle's 3D surroundings. Radar, with its robustness to adverse weat…
CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation
Huawei Sun, Hao Feng, Julius Ott +2
Depth estimation is critical in autonomous driving for interpreting 3D scenes accurately. Recently, radar-camera depth estimation has become of sufficient interest due to the robus…
MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection
Xiangyuan Peng, Miao Tang, Huawei Sun +3
In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to L…
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Huawei Sun, Hao Feng, Gianfranco Mauro +4
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information d…
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resour…
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domai…