arXiv:2006.16796
공식 코드 · Papers with Code
Leveraging Temporal Information for 3D Detection and Domain Adaptation
원문 초록
Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds. However, recent progress largely focuses on detecting objects in a single 360-degree sweep, without extensively exploring the temporal information. In this report, we describe a simple way to pass such information in the learning pipeline by adding timestamps to the point clouds, which shows consistent improvements across all three classes.
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Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
RoCA: Robust Cross-Domain End-to-End Autonomous Driving
Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
토론
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