D-BDM reduces update time and memory in LiDAR 3D occupancy mapping by restricting ray casting to boundary exteriors and enabling direct boundary updates.
HeLiPR: Heterogeneous LiDAR Dataset for Inter-LiDAR Place Recognition Under Spatiotemporal Variations.The In- ternational Journal of Robotics Research, 43(3):1867– 1883
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
Point cloud geometry is cast as a statistical manifold of per-point Gaussians, with POLI learning the mapping self-supervisedly to improve perception without labeled data.
citing papers explorer
-
D-BDM: A Direct and Efficient Boundary-Based Occupancy Grid Mapping Framework for LiDARs
D-BDM reduces update time and memory in LiDAR 3D occupancy mapping by restricting ray casting to boundary exteriors and enabling direct boundary updates.
-
Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice
Point cloud geometry is cast as a statistical manifold of per-point Gaussians, with POLI learning the mapping self-supervisedly to improve perception without labeled data.