To overcome this limitation, we propose Neural Manifold Representation (NMR), a representation for the task of autonomous driving that learns to infer semantics and predict way-points on a manifold over a finite horizon, centered on the ego-vehicle.
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios.
Ranked #2 on 3D Multi-Object Tracking on KITTI
ECHO can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources.
Distributed, Parallel, and Cluster Computing