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Found 6 papers, 6 papers with code
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation.
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.
Real-time Collision Risk Estimation based on Stochastic Reachability Spaces
Estimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios.
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network.
Ranked #1 on
Object Localization
on KITTI Pedestrian Easy
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles
Ground plane estimation and ground point seg-mentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping.
Attentional PointNet for 3D-Object Detection in Point Clouds
In this study, we propose Attentional Point- Net, which is a novel end-to-end trainable deep architecture for object detection in point clouds.
