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Found 5 papers, 3 papers with code
COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years.
ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene.
Domain generalization of 3D semantic segmentation in autonomous driving
This method relies on leveraging the geometry and sequentiality of the LiDAR data to enhance its generalization performances by working on partially accumulated point clouds.
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets
Transfer learning is a proven technique in 2D computer vision to leverage the large amount of data available and achieve high performance with datasets limited in size due to the cost of acquisition or annotation.
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
We tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph.
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