Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud

Benchmark Model Rank Results
3d-part-segmentation-on-shapenet-partPoint-JEPA#50Instance Average IoU: 83.9Class Average IoU: 85.8
3d-point-cloud-classification-on-modelnet40Point-JEPA (voting)#24Overall Accuracy: 94.1±0.1
3d-point-cloud-classification-on-modelnet40Point-JEPA (no voting)#44Overall Accuracy: 93.8±0.2
3d-point-cloud-classification-on-scanobjectnnPoint-JEPA#43Overall Accuracy: 86.6OBJ-BG (OA): 92.9±0.4
3d-point-cloud-linear-classification-onPoint-JEPA#1Overall Accuracy: 93.7±0.2
few-shot-3d-point-cloud-classification-on-1Point-JEPA#4Overall Accuracy: 97.4Standard Deviation: 2.2
few-shot-3d-point-cloud-classification-on-2Point-JEPA#2Overall Accuracy: 99.2Standard Deviation: 0.8
few-shot-3d-point-cloud-classification-on-3Point-JEPA#1Overall Accuracy: 95.0Standard Deviation: 3.6
few-shot-3d-point-cloud-classification-on-4Point-JEPA#2Overall Accuracy: 96.4Standard Deviation: 2.7