Few Shot 3D Point Cloud Classification On 4

المقاييس

Overall Accuracy

Standard Deviation

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

جدول المقارنة

اسم النموذج	Overall Accuracy	Standard Deviation
point-lgmask-local-and-global-contexts	95.1	3.4
self-supervised-few-shot-learning-on-point	50.10	5.0
autoencoders-as-cross-modal-teachers-can	95.6	2.8
pointgpt-auto-regressively-generative-pre-1	96.1	2.8
masked-autoencoders-for-point-cloud-self	95.0	3.0
gpr-net-geometric-prototypical-network-for	73.8	2.0
crossmoco-multi-modal-momentum-contrastive	91.0	3.4
gpr-net-geometric-prototypical-network-for	63.3	2.2
pcp-mae-learning-to-predict-centers-for-point	95.9	2.7
rethinking-masked-representation-learning-for	95.6	2.6
shapellm-universal-3d-object-understanding	96.5	3.0
pointnet-deep-learning-on-point-sets-for-3d	35.20	13.5
pointnet-deep-hierarchical-feature-learning	18.80	7.0
pointcnn-convolution-on-x-transformed-points	49.95	7.2
gpr-net-geometric-prototypical-network-for	63.4	2.0
dynamic-graph-cnn-for-learning-on-point	16.9	1.5
regress-before-construct-regress-autoencoder	95.8	3.0
pre-training-by-completing-point-clouds	89.7	1.5
point-m2ae-multi-scale-masked-autoencoders	95.0	3.0
instance-aware-dynamic-prompt-tuning-for-pre	95.4	-
masked-discrimination-for-self-supervised	93.4	3.5
gpr-net-geometric-prototypical-network-for	72.8	1.8
towards-compact-3d-representations-via-point	95.8	-
3d-jepa-a-joint-embedding-predictive	96.3	2.4
contrast-with-reconstruct-contrastive-3d	95.8	3.0
pre-training-by-completing-point-clouds	86.5	2.2
point-bert-pre-training-3d-point-cloud	92.7	5.1
point-jepa-a-joint-embedding-predictive	96.4	2.7
self-supervised-few-shot-learning-on-point	53.00	4.1
point2vec-for-self-supervised-representation	95.8	3.1
learning-3d-representations-from-2d-pre	95.5	3.0