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Research map / Detection and segmentation / 3D detection and driving

PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

CVPR 2019 · Detection and segmentation › 3D detection and driving · approach: 3D objectness and scene segmentation · 700 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use detection-by-segmentation with jointly predicted disjoint instance masks and confidence scores instead of SGPN's similarity-based groupingPointNet++ for PartNet instance segmentation · PartNetpart-category mAP (%): 46.8 -> 54.4Helped
Use ensemble inference across segmentation levels rather than bottom-up inferencePointNet++ hierarchical semantic segmentation · PartNetpart-category mIoU (%): 51.2 -> 51.7Helped
Use ensemble inference across segmentation levels rather than top-down inferencePointNet++ hierarchical semantic segmentation · PartNetpart-category mIoU (%): 50.8 -> 51.7Helped
Refine hierarchical templates using multiple annotations to merge confusing concepts or remove hard-to-distinguish partsPartNet annotation templates · PartNet multiple-annotation subsetannotation consistency (%): 69.8 -> 83.3; 13.5% improvementHelped
Use PointCNN rather than PointNet++Fine-grained semantic segmentation · PartNetpart-category mIoU (%): 58.1 -> 59.8Helped
Use PointNet++ rather than PointCNNFine-grained semantic segmentation · PartNetshape mIoU (%): 68.1 -> 66.3Helped

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