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Found 5 papers, 1 papers with code
Label Efficient Visual Abstractions for Autonomous Driving
Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds
In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network.
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e. g., at texture-less or reflective surfaces.
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes.
Optimizing Average Precision using Weakly Supervised Data
The performance of binary classification tasks, such as action classification and object detection, is often measured in terms of the average precision (AP).
