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ResearchFormerly WizWand

Point Cloud Object Recognition on ScanObjectNN OBJ_ONLY (test)

92.94AccuracyPointGST
87.927289.228690.5391.8314Dec 31, 2025
Updated 8mo ago

Evaluation results

MethodLinks
PointGSTPre-trained model=RECON [62], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.1292.94
FullPre-trained model=RECON [62], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.1292.77
CLoRAPre-trained model=RECON [62], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.4 (1.81%), #GFLOPs=4.76(-)2025.1292.77
DAPTPre-trained model=RECON [62], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.1292.43
IDPTPre-trained model=RECON [62], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.1291.57
PointLoRAPre-trained model=RECON [62], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.8 (3.43%), #GFLOPs=5.06(↑)2025.1291.22
CLoRAPre-trained model=Point-BERT [60], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.3 (1.36%), #GFLOPs=4.76(-)2025.1290.36
DAPTPre-trained model=Point-MAE [61], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.1290.19
PointGSTPre-trained model=Point-MAE [61], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.1290.19
CLoRAPre-trained model=Point-MAE [61], Fine-tuning strategy=CLoRA(ours), #Param. (M)=0.3 (1.36%), #GFLOPs=4.76(-)2025.1290.19
Point-PEFTPre-trained model=RECON [62], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.1290.19
IDPTPre-trained model=Point-MAE [61], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.1290.02
Point-PEFTPre-trained model=Point-BERT [60], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.1289.67
DAPTPre-trained model=Point-BERT [60], Fine-tuning strategy=DAPT [41], #Param. (M)=1.1 (4.97%), #GFLOPs=4.96(↑)2025.1289.67
PointGSTPre-trained model=Point-BERT [60], Fine-tuning strategy=PointGST [56], #Param. (M)=0.6 (2.77%), #GFLOPs=4.81(↑)2025.1289.67
PointLoRAPre-trained model=Point-MAE [61], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.8 (3.43%), #GFLOPs=5.06(↑)2025.1289.33
Point-PEFTPre-trained model=Point-MAE [61], Fine-tuning strategy=Point-PEFT [63], #Param. (M)=0.7 (3.13%), #GFLOPs=7.61(↑)2025.1288.98
PointLoRAPre-trained model=Point-BERT [60], Fine-tuning strategy=PointLoRA [64], #Param. (M)=0.9 (4.07%), #GFLOPs=5.06(↑)2025.1288.98
IDPTPre-trained model=Point-BERT [60], Fine-tuning strategy=IDPT [55], #Param. (M)=1.7 (7.69%), #GFLOPs=7.10(↑)2025.1288.3
FullPre-trained model=Point-MAE [61], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.1288.29
FullPre-trained model=Point-BERT [60], Fine-tuning strategy=Full, #Param. (M)=22.1 (100%), #GFLOPs=4.762025.1288.12