Dynamic Graph CNN for Learning on Point Clouds

Benchmark Model Rank Results
3d-part-segmentation-on-shapenet-partDGCNN#42Instance Average IoU: 85.2
3d-point-cloud-classification-on-intraDGCNN#11F1 score (5-fold): 0.738
3d-point-cloud-classification-on-modelnet40DGCNN#72Overall Accuracy: 92.9Mean Accuracy: 90.2Number of params: 1.81M
3d-point-cloud-classification-on-modelnet40-cDGCNN#8Error Rate: 0.259
3d-point-cloud-classification-on-scanobjectnnDGCNN#68Overall Accuracy: 78.1Mean Accuracy: 73.6OBJ-BG (OA): 82.8
few-shot-3d-point-cloud-classification-on-1DGCNN#29Overall Accuracy: 31.6Standard Deviation: 9.0
few-shot-3d-point-cloud-classification-on-2DGCNN#29Overall Accuracy: 40.8Standard Deviation: 14.6
few-shot-3d-point-cloud-classification-on-3DGCNN#30Overall Accuracy: 19.85Standard Deviation: 6.5
few-shot-3d-point-cloud-classification-on-4DGCNN#30Overall Accuracy: 16.9Standard Deviation: 1.5
point-cloud-classification-on-pointcloud-cDGCNN#17mean Corruption Error (mCE): 1.000
point-cloud-segmentation-on-pointcloud-cDGCNN#6mean Corruption Error (mCE): 1.000
supervised-only-3d-point-cloud-classificationDGCNN#9Overall Accuracy (PB_T50_RS): 78.1GFLOPs: 2.4