| semantic-segmentation-on-toronto-3d-l002 | PointNet++ | #2 | oAcc: 91.2mIoU: 56.5 |
| 3d-semantic-segmentation-on-kitti-360 | PointNet++ | #3 | miou: 35.66mIoU Category: 58.28Model size: 3.0M |
| 3d-point-cloud-classification-on-intra | PointNet++ | #4 | F1 score (5-fold): 0.903 |
| semantic-segmentation-on-shapenet | PointNet++ | #4 | Mean IoU: 84.6% |
| 3d-part-segmentation-on-intra | PointNet++ | #5 | IoU (V): 93.42IoU (A): 76.38DSC (V): 96.48DSC (A): 84.64 |
| 3d-semantic-segmentation-on-dales | PointNet++ | #5 | mIoU: 68.3Overall Accuracy: 95.7Model size: 3.0M |
| 3d-point-cloud-classification-on-modelnet40-c | PointNet++ | #6 | Error Rate: 0.236 |
| 3d-semantic-segmentation-on-stpls3d | PointNet++ | #6 | mIOU: 15.92 |
| point-cloud-segmentation-on-pointcloud-c | PointNet++ | #8 | mean Corruption Error (mCE): 1.112 |
| supervised-only-3d-point-cloud-classification-on-scanobjectnn | PointNet++ | #11 | Overall Accuracy (PB_T50_RS): 77.9GFLOPs: 1.7… |
| semantic-segmentation-on-scannet | PointNet++ | #34 | val mIoU: 53.5test mIoU: 33.9 |
| 3d-semantic-segmentation-on-semantickitti | PointNet++ | #36 | test mIoU: 20.1% |
| few-shot-3d-point-cloud-classification-on-modelnet40-5-way-10-shot | PointNet++ | #36 | Overall Accuracy: 38.53Standard Deviation: 16.0 |
| few-shot-3d-point-cloud-classification-on-modelnet40-5-way-20-shot | PointNet++ | #36 | Overall Accuracy: 42.39Standard Deviation: 14.2 |
| few-shot-3d-point-cloud-classification-on-modelnet40-10-way-20-shot | PointNet++ | #37 | Overall Accuracy: 18.80Standard Deviation: 7.0 |
| few-shot-3d-point-cloud-classification-on-modelnet40-10-way-10-shot | PointNet++ | #38 | Overall Accuracy: 23.05Standard Deviation: 7.0 |
| 3d-part-segmentation-on-shapenet-part | PointNet++ | #51 | Instance Average IoU: 85.1Class Average IoU: 81.9 |
| 3d-point-cloud-classification-on-scanobjectnn | PointNet++ | #72 | Overall Accuracy: 77.9Mean Accuracy: 75.4OBJ-BG (OA): 82.3… |
| person-re-identification-on-dukemtmc-reid | PointNet++ (MSG) [qi2017pointnet++] | #81 | mAP: 39.36Rank-1: 60.23 |
| 3d-point-cloud-classification-on-modelnet40 | PointNet++ | #98 | Overall Accuracy: 90.7Number of params: 1.74M |