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On Feature Relevance Uncertainty: A Monte Carlo Dropout Sampling Approach
Understanding decisions made by neural networks is key for the deployment of intelligent systems in real world applications.
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset.
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks
The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection.
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