Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation

arXiv:2404.06124v1 Announce Type: new
Abstract: Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings. The predictive performance of any classification model strongly depends on the underlying dataset and the prior knowledge conveyed by the annotated labels. While the labels provide a basis for the learning process, they usually fail to represent inherent relations between the classes - representations, which are a natural element of the human perception system. We …

abstract algorithms applications arxiv autonomous autonomous driving call classification classification model cs.ai cs.cv cs.ro dataset diverse driving environment hierarchical insights knowledge labels perception performance predictive prior robust safety safety-critical segmentation semantic type