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Found 6 papers, 0 papers with code
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.
Diverse Complexity Measures for Dataset Curation in Self-driving
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
End-to-end Contextual Perception and Prediction with Interaction Transformer
We show that our approach can outperform the state-of-the-art on both datasets.
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Particularly difficult is the prediction of human behavior.
