D-BDM reduces update time and memory in LiDAR 3D occupancy mapping by restricting ray casting to boundary exteriors and enabling direct boundary updates.
Real-time 3d reconstruction at scale using voxel hashing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
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.
-
VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.