Point Cloud Object Recognition on ScanObjectNN OBJ_ONLY (test)
92.94AccuracyPointGST
Evaluation Results
| Method | Links | |
|---|---|---|
| PointGSTPre-trained model=RECON [62], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.12 | 92.94 | |
| FullPre-trained model=RECON [62], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.12 | 92.77 | |
| CLoRAPre-trained model=RECON [62], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.4 (1.81%), #GFLOPs=4.76(-)2025.12 | 92.77 | |
| DAPTPre-trained model=RECON [62], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.12 | 92.43 | |
| IDPTPre-trained model=RECON [62], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.12 | 91.57 | |
| PointLoRAPre-trained model=RECON [62], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.8 (3.43%), #GFLOPs=5.06(↑)2025.12 | 91.22 | |
| CLoRAPre-trained model=Point-BERT [60], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.3 (1.36%), #GFLOPs=4.76(-)2025.12 | 90.36 | |
| DAPTPre-trained model=Point-MAE [61], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.12 | 90.19 | |
| PointGSTPre-trained model=Point-MAE [61], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.12 | 90.19 | |
| CLoRAPre-trained model=Point-MAE [61], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.3 (1.36%), #GFLOPs=4.76(-)2025.12 | 90.19 | |
| Point-PEFTPre-trained model=RECON [62], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.12 | 90.19 | |
| IDPTPre-trained model=Point-MAE [61], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.12 | 90.02 | |
| Point-PEFTPre-trained model=Point-BERT [60], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.12 | 89.67 | |
| DAPTPre-trained model=Point-BERT [60], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.12 | 89.67 | |
| PointGSTPre-trained model=Point-BERT [60], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.12 | 89.67 | |
| PointLoRAPre-trained model=Point-MAE [61], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.8 (3.43%), #GFLOPs=5.06(↑)2025.12 | 89.33 | |
| Point-PEFTPre-trained model=Point-MAE [61], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.12 | 88.98 | |
| PointLoRAPre-trained model=Point-BERT [60], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.9 (4.07%), #GFLOPs=5.06(↑)2025.12 | 88.98 | |
| IDPTPre-trained model=Point-BERT [60], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.12 | 88.3 | |
| FullPre-trained model=Point-MAE [61], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.12 | 88.29 | |
| FullPre-trained model=Point-BERT [60], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.12 | 88.12 |