VRS generates annotated roadside LiDAR data from vehicle observations via novel view synthesis with geometry completion and occupancy constraints, improving 3D object detection generalization.
Large-scale lidar consistent mapping using hierarchical lidar bundle adjustment
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QCSA reduces inserted loop factors 3.8 times and raises precision from 0.542 to 0.717 on the SNULib dataset while lowering worst-case trajectory error compared with dense Top-1+G-ICP baselines.
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Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
VRS generates annotated roadside LiDAR data from vehicle observations via novel view synthesis with geometry completion and occupancy constraints, improving 3D object detection generalization.
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Query-Calibrated Segmental Admission for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments
QCSA reduces inserted loop factors 3.8 times and raises precision from 0.542 to 0.717 on the SNULib dataset while lowering worst-case trajectory error compared with dense Top-1+G-ICP baselines.