| 3d-semantic-segmentation-on-dales | KPConv | #1 | mIoU: 81.1Overall Accuracy: 97.8Model size: 14M |
| 3d-semantic-segmentation-on-stpls3d | KpConv | #1 | mIOU: 53.73 |
| robust-3d-semantic-segmentation-on-robo3d | KPConv | #2 | mean Corruption Error (mCE): 99.54% |
| 3d-semantic-segmentation-on-sensaturban | KPConv | #4 | mIoU: 57.58 |
| lidar-semantic-segmentation-on-paris-lille-3d | KPConv deform | #4 | mIOU: 0.759 |
| semantic-segmentation-on-semantic3d | KPConv | #7 | mIoU: 74.6% |
| 3d-semantic-segmentation-on-semantickitti | KPConv | #22 | test mIoU: 58.8% |
| 3d-part-segmentation-on-shapenet-part | KPConv | #24 | Instance Average IoU: 86.4Class Average IoU: 85.1 |
| semantic-segmentation-on-s3dis | KPConv | #27 | Mean IoU: 70.6mAcc: 79.1Number of params: 14.1MParams (M): 14.1 |
| semantic-segmentation-on-scannet | KpConv | #30 | val mIoU: 69.2test mIoU: 68.0 |
| semantic-segmentation-on-s3dis-area5 | KPConv | #39 | mIoU: 67.1mAcc: 72.8Number of params: 14.1M |
| 3d-point-cloud-classification-on-modelnet40 | KPConv | #77 | Overall Accuracy: 92.9 |