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Research map / Point clouds / Segmentation and detection

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

NeurIPS 2019 · Point clouds › Segmentation and detection · approach: Point cloud semantic segmentation · 262 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the box score prediction sub-branch (ablation)3D-BoNet · S3DIS Area 5mPrec: 50.9 -> 57.5Helped
use sIoU alone rather than Euclidean distance alone for box association and loss (ablation)3D-BoNet · S3DIS Area 5mPrec: 53.8 -> 55.2Helped
use sIoU alone rather than cross-entropy score alone for box association and loss (ablation)3D-BoNet · S3DIS Area 5mPrec: 51.8 -> 55.2Helped
combine Euclidean distance, sIoU, and cross-entropy score instead of using sIoU alone (ablation)3D-BoNet · S3DIS Area 5mPrec: 55.2 -> 57.5Helped
add box prediction supervision (ablation)3D-BoNet · S3DIS Area 5mPrec: 37.3 -> 57.5Helped
use focal loss instead of standard cross-entropy for point mask prediction (ablation)3D-BoNet · S3DIS Area 5mPrec: 50.8 -> 57.5Helped
use 3D-BoNet rather than the strongest reported baselineScanNet(v2) · ScanNet(v2)AP@0.5 (mean): 48.1 -> 48.8Helped
use 3D-BoNet rather than ASISS3DIS · S3DISmPrec: 63.6 -> 65.6Helped
use 3D-BoNet rather than ASISS3DIS · S3DISmRec: 47.5 -> 47.6No clear effect
use 3D-BoNet rather than ASISScanNet(v2) validation split · ScanNet(v2) validation splittotal time: 56757 -> 2871Helped

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