D-BDM reduces update time and memory in LiDAR 3D occupancy mapping by restricting ray casting to boundary exteriors and enabling direct boundary updates.
Vision meets robotics: The kitti dataset
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
RAFT-MSF++ recurrently fuses Geometry-Motion Features across frames with positional attention and occlusion regularization to improve self-supervised monocular scene flow estimation.
Monocular depth estimation is recast as indirect feature restoration via an invertible diffusion module plus auxiliary viewpoint enhancement, delivering 4-38% RMSE gains on KITTI over baselines.
citing papers explorer
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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.
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LiftFormer: Lifting and Frame Theory Based Monocular Depth Estimation Using Depth and Edge Oriented Subspace Representation
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
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RAFT-MSF++: Temporal Geometry-Motion Feature Fusion for Self-Supervised Monocular Scene Flow
RAFT-MSF++ recurrently fuses Geometry-Motion Features across frames with positional attention and occlusion regularization to improve self-supervised monocular scene flow estimation.
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Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach
Monocular depth estimation is recast as indirect feature restoration via an invertible diffusion module plus auxiliary viewpoint enhancement, delivering 4-38% RMSE gains on KITTI over baselines.
- LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation