MotionNet is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.
Source: MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View MapsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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3D Object Detection | 1 | 25.00% |
Autonomous Driving | 1 | 25.00% |
motion prediction | 1 | 25.00% |
Object Detection | 1 | 25.00% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |