1 research outputs found
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles
Autonomous driving (AD) perception today relies heavily on deep learning
based architectures requiring large scale annotated datasets with their
associated costs for curation and annotation. The 3D semantic data are useful
for core perception tasks such as obstacle detection and ego-vehicle
localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg),
with a diverse label space corresponding to a large scale production grade
operational domain, including rural, urban, industrial sites and universities
from 13 countries. It contains 23 labeled sequences and 25 supplementary
sequences without labels, designed to explore self-supervised and
semi-supervised semantic segmentation benchmarks on point clouds. We also
propose a novel method for sequential dataset split generation based on
iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU
improvement over the original split proposed by SemanticKITTI dataset. A
complete benchmark for semantic segmentation task was performed, with state of
the art methods. Finally, we demonstrate an active learning (AL) based dataset
distillation framework. We introduce a novel heuristic-free sampling method
called distance sampling in the context of AL. A detailed presentation on the
dataset is available at https://www.youtube.com/watch?v=5m6ALIs-s20 .Comment: Submitted to RA-L. Version with supplementary material