TopoHR proposes a hierarchical centerline representation and topology reasoning module with point-to-instance relations and cyclic interactions, achieving new state-of-the-art results on the OpenLane-V2 benchmark for autonomous driving.
nuscenes: A multi- modal dataset for autonomous driving
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
TopoMaskV3 adds dense offset and height heads to produce standalone 3D road centerlines from masks and reports 28.5 OLS on a new geographically disjoint long-range benchmark.
ChronoTrack enables effective long-term 3D single-object tracking in LiDAR by storing target features in compact learnable memory tokens regularized by temporal consistency and memory-cycle consistency losses, reaching SOTA accuracy at 42 FPS.
citing papers explorer
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TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
TopoHR proposes a hierarchical centerline representation and topology reasoning module with point-to-instance relations and cyclic interactions, achieving new state-of-the-art results on the OpenLane-V2 benchmark for autonomous driving.
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TopoMaskV3: 3D Mask Head with Dense Offset and Height Predictions for Road Topology Understanding
TopoMaskV3 adds dense offset and height heads to produce standalone 3D road centerlines from masks and reports 28.5 OLS on a new geographically disjoint long-range benchmark.
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Temporally Consistent Long-Term Memory for 3D Single Object Tracking
ChronoTrack enables effective long-term 3D single-object tracking in LiDAR by storing target features in compact learnable memory tokens regularized by temporal consistency and memory-cycle consistency losses, reaching SOTA accuracy at 42 FPS.