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Contrastive Boundary Learning for Point Cloud Segmentation

CVPR 2022 · Point clouds › Segmentation and detection · approach: Point cloud semantic segmentation · 202 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add CBL to RandLA-NetRandLA-Net · Semantic3D reduced-8mIoU: 77.4 -> 78.4Helped
Add CBL to ConvNetConvNet · S3DIS Area 5mIoU: 67.4 -> 69.4Helped
Add CBL to ConvNetConvNet · S3DIS 6-foldmIoU: 69.7 -> 73.1Helped
Add CBL to ConvNetConvNet · ScanNetmIoU: 69.1 -> 70.5Helped
Add CBL at input and sub-scenes rather than at input only (ablation)ConvNet · ScanNet validationmIoU: 70.05 -> 70.98Helped
Add a multiscale head without CBL (ablation)ConvNet · ScanNet validationOA: 88.97 -> 88.88Hurt
Add a multiscale head to ConvNet with CBL (ablation)ConvNet + CBL · ScanNet validationmIoU: 70.98 -> 71.33Helped
Set CBL temperature to 1 instead of 10 (ablation)ConvNet + CBL · ScanNet validationmIoU: 70.03 -> 71.33Helped
Use nearest-point labels for sub-sampled points instead of pooled label distributions (ablation)ConvNet + CBL · S3DIS Area 5mIoU: 69.4 -> 68.3Hurt
Replace pooled label distributions with hard argmax labels (ablation)ConvNet + CBL · S3DIS Area 5mIoU: 69.4 -> 66.8Hurt
Use KL-distance thresholding for sub-scene boundary determination (ablation)ConvNet + CBL · S3DIS Area 5mIoU: 69.4 -> 69.5Helped
Add CBL to baselinesRandLA-Net, CloserLook3D, and ConvNet · S3DIS Area 5 boundary and inner areasmIoU: boundary: +3.3, +0.6, +2.5; inner: +1.4, +0.2, +2.0Helped

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