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Research map / Detection and segmentation / Open-set and few-shot detection

HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

CVPR 2016 · Detection and segmentation › Open-set and few-shot detection · approach: Amodal and open-world instance segmentation · 150 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the parallel-road constraint to the road parsing model (ablation)LaneRoad · Air-Ground-KITTI testAverage IoU: 45.4 -> 48.6Helped
Add along-road smoothness to the model without the parallel-road constraint (ablation)Lane · Air-Ground-KITTI testAverage IoU: 43.6 -> 45.4Helped
Add the parallel-road constraint to the model without along-road smoothness (ablation)Lane · Air-Ground-KITTI testAverage IoU: 43.6 -> 44.8Helped
Add ground-image potentials to LaneRoadParallel (ablation)LaneRoadParallel · Air-Ground-KITTI test, areas with ground imagesAverage IoU: 41.9 -> 42.0No clear effect
Jointly infer ground-to-aerial alignment from ground and aerial evidenceGround-to-air alignment · Air-Ground-KITTIAlignment error (m): 1.67 -> 0.57Helped
Use the parallel-road constraint in fine-grained road parsing (ablation)LaneRoadParallel · Air-Ground-KITTI testRoad IoU: 61.9 -> 68.0Helped

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