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Research map / Point clouds / Segmentation and detection

GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud

CVPR 2019 · Point clouds › Segmentation and detection · approach: Point cloud semantic segmentation · 348 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use R-PointNet instead of SGPN-CNNNYUv2 instance segmentation · NYUv2AP: 33.6 -> 39.3Helped
Use R-PointNet instead of SGPNPartNet part instance segmentation · PartNet ChairAP: 0.194 -> 0.268Helped
Replace direct box regression proposals with GSPN (ablation)R-PointNet · ScanNet v2 validationAP: 15.8 -> 19.3Helped
Replace binary-segmentation-based proposals with GSPN (ablation)R-PointNet · ScanNet v2 validationAP: 14.9 -> 19.3Helped
Use CVAE rather than a naive encoder-decoder (ablation)GSPN · ScanNet v2 validationmIoU: 0.408 -> 0.581Helped
Use multi-scale rather than single-scale context (ablation)GSPN · ScanNet v2 validationmIoU: 0.486 -> 0.581Helped
Predict the object center before generation (ablation)GSPN · ScanNet v2 validationmIoU: 0.409 -> 0.581Helped
Include semantic feature f_sem (ablation)R-PointNet feature backbone · ScanNet v2 validationAP: 0.161 -> 0.191Helped
Include instance-sensitive context feature f_hat_c (ablation)R-PointNet feature backbone · ScanNet v2 validationAP: 0.178 -> 0.191Helped
Pretrain the semantic feature network (ablation)R-PointNet feature backbone · ScanNet v2 validationAP: 0.180 -> 0.191Helped
Use GSPN rather than direct box regression proposals (ablation)R-PointNet · ScanNet v2 validationmIoU: 0.514 -> 0.581Helped

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