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Found 9 papers, 2 papers with code
Learning to Drive by Observing the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
MV2MAE: Multi-View Video Masked Autoencoders
We report state-of-the-art results on the NTU-60, NTU-120 and ETRI datasets, as well as in the transfer learning setting on NUCLA, PKU-MMD-II and ROCOG-v2 datasets, demonstrating the robustness of our approach.
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency.
Attentional Bottleneck: Towards an Interpretable Deep Driving Network
Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars.
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
Predicting human behavior is a difficult and crucial task required for motion planning.
Ranked #2 on
Trajectory Prediction
on PAID
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
Geometric Urban Geo-Localization
We propose a purely geometric correspondence-free approach to urban geo-localization using 3D point-ray features extracted from the Digital Elevation Map of an urban environment.
Geometric Polynomial Constraints in Higher-Order Graph Matching
In this paper, we address the problem of finding correspondences in the absence of unary or pairwise constraints as it emerges in problems where unary appearance similarity like SIFT matches is not available.
Joint Spectral Correspondence for Disparate Image Matching
We address the problem of matching images with disparate appearance arising from factors like dramatic illumination (day vs. night), age (historic vs. new) and rendering style differences.
