PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Benchmark Model Rank Results
3d-part-segmentation-on-intraPointNet#7IoU (V): 75.23IoU (A): 37.75DSC (V): 85.00DSC (A): 49.59
3d-part-segmentation-on-shapenet-partPointNet#51Instance Average IoU: 83.7
3d-point-cloud-classification-on-intraPointNet#12F1 score (5-fold): 0.684
3d-point-cloud-classification-on-modelnet40PointNet#98Overall Accuracy: 89.2Mean Accuracy: 86.0Number of params: 3.47M
3d-point-cloud-classification-on-modelnet40-cPointNet#11Error Rate: 0.283
3d-point-cloud-classification-on-scanobjectnnPointNet#71Overall Accuracy: 68.2Mean Accuracy: 63.4
3d-semantic-segmentation-on-kitti-360PointNet#4miou: 13.07mIoU Category: 30.42Model size: N/A
3d-semantic-segmentation-on-semantickittiPointNet#35test mIoU: 14.6%
few-shot-3d-point-cloud-classification-on-1PointNet#27Overall Accuracy: 51.97Standard Deviation: 12.1
few-shot-3d-point-cloud-classification-on-2PointNet#27Overall Accuracy: 57.81Standard Deviation: 15.5
few-shot-3d-point-cloud-classification-on-3PointNet#27Overall Accuracy: 46.60Standard Deviation: 13.5
few-shot-3d-point-cloud-classification-on-4PointNet#28Overall Accuracy: 35.20Standard Deviation: 13.5
point-cloud-classification-on-pointcloud-cPointNet#24mean Corruption Error (mCE): 1.422
point-cloud-segmentation-on-pointcloud-cPointNet#11mean Corruption Error (mCE): 1.178
semantic-segmentation-on-s3disPointNet#52mAcc: 66.2Number of params: N/A
semantic-segmentation-on-s3dis-area5PointNet#46mAcc: 49.0Number of params: N/A
supervised-only-3d-point-cloud-classificationPointNet#12Overall Accuracy (PB_T50_RS): 68.0GFLOPs: 0.5