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Multi-camera Bird's Eye View Perception for Autonomous Driving
David Unger, Nikhil Gosala, Varun Ravi Kumar +3
Most automated driving systems comprise a diverse sensor set, including several cameras, Radars, and LiDARs, ensuring a complete 360°coverage in near and far regions. Unlike Radar…
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
Sambit Mohapatra, Senthil Yogamani, Varun Ravi Kumar +3
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning acr…
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Shubhankar Borse, Senthil Yogamani, Marvin Klingner +4
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
XKD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar +4
Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images…
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements
Shihao Shen, Louis Kerofsky, Senthil Yogamani
Optical flow estimation is a well-studied topic for automated driving applications. Many outstanding optical flow estimation methods have been proposed, but they become erroneous w…