6 papers
Radar-Camera BEV Multi-Task Learning with Cross-Task Attention Bridge for Joint 3D Detection and Segmentation
Ahmet İnanç, Özgür Erkent
Bird's-eye-view (BEV) representations are the dominant paradigm for 3D perception in autonomous driving, providing a unified spatial canvas where detection and segmentation feature…
Frequency-Guided Fusion For RGB-Thermal Semantic Segmentation
İsmail Emre Canıtez, Özgür Erkent
Semantic segmentation in complex environments such as urban driving scenes remains challenging under adverse lighting conditions, where RGB images alone provide insufficient inform…
Sum of Costs Diffusion with Dynamic Guidance for Motion Planning
Aysu Aylin Kaplan, Özgür Erkent
The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant challenges in generalizing to…
Multi-Task Learning for Robot Perception with Imbalanced Data
Ozgur Erkent
Multi-task problem solving has been shown to improve the accuracy of the individual tasks, which is an important feature for robots, as they have a limited resource. However, when…
LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction
Manuel Alejandro Diaz-Zapata, David Sierra González, Özgür Erkent +2
Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this…
LAPTNet: LiDAR-Aided Perspective Transform Network
Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier +2
Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vit…