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Found 5 papers, 2 papers with code
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial-temporal priors.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU ped - 224x480 - Vis filter. - 100x100 at 0.5 metric)
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication.
Ranked #1 on
Monocular 3D Object Detection
on CoPerception-UAVs
Aerial Monocular 3D Object Detection
To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space.
