PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Benchmark Model Rank Results
3d-part-segmentation-on-intraPointNet++#5IoU (V): 93.42IoU (A): 76.38DSC (V): 96.48DSC (A): 84.64
3d-part-segmentation-on-shapenet-partPointNet++#43Instance Average IoU: 85.1Class Average IoU: 81.9
3d-point-cloud-classification-on-intraPointNet++#4F1 score (5-fold): 0.903
3d-point-cloud-classification-on-modelnet40PointNet++#94Overall Accuracy: 90.7Number of params: 1.74M
3d-point-cloud-classification-on-modelnet40-cPointNet++#6Error Rate: 0.236
3d-point-cloud-classification-on-scanobjectnnPointNet++#69Overall Accuracy: 77.9Mean Accuracy: 75.4OBJ-BG (OA): 82.3
3d-semantic-segmentation-on-dalesPointNet++#5mIoU: 68.3Overall Accuracy: 95.7Model size: 3.0M
3d-semantic-segmentation-on-kitti-360PointNet++#3miou: 35.66mIoU Category: 58.28Model size: 3.0M
3d-semantic-segmentation-on-semantickittiPointNet++#32test mIoU: 20.1%
3d-semantic-segmentation-on-stpls3dPointNet++#6mIOU: 15.92
few-shot-3d-point-cloud-classification-on-1PointNet++#28Overall Accuracy: 38.53Standard Deviation: 16.0
few-shot-3d-point-cloud-classification-on-2PointNet++#28Overall Accuracy: 42.39Standard Deviation: 14.2
few-shot-3d-point-cloud-classification-on-3PointNet++#29Overall Accuracy: 23.05Standard Deviation: 7.0
few-shot-3d-point-cloud-classification-on-4PointNet++#29Overall Accuracy: 18.80Standard Deviation: 7.0
person-re-identification-on-dukemtmc-reidPointNet++ (MSG) [qi2017pointnet++]#81mAP: 39.36Rank-1: 60.23
point-cloud-segmentation-on-pointcloud-cPointNet++#8mean Corruption Error (mCE): 1.112
semantic-segmentation-on-scannetPointNet++#30val mIoU: 53.5test mIoU: 33.9
semantic-segmentation-on-shapenetPointNet++#4Mean IoU: 84.6%
semantic-segmentation-on-toronto-3d-l002PointNet++#2oAcc: 91.2mIoU: 56.5
supervised-only-3d-point-cloud-classificationPointNet++#10Overall Accuracy (PB_T50_RS): 77.9GFLOPs: 1.7