3D Point Cloud Classification On Modelnet40

Métriques

Overall Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Overall Accuracy	Paper Title	Repository
PCNN	92.3	Point Convolutional Neural Networks by Extension Operators	-
Point Cloud Transformer	93.2	PCT: Point cloud transformer	-
PointNet2+PointCMT	94.4	Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis	-
Point-MAE	94.0	Masked Autoencoders for Point Cloud Self-supervised Learning	-
PointConT	93.5	Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space	-
InterpCNN	93.0	Interpolated Convolutional Networks for 3D Point Cloud Understanding	-
point2vec	94.8	Point2Vec for Self-Supervised Representation Learning on Point Clouds	-
RS-CNN	92.9	Relation-Shape Convolutional Neural Network for Point Cloud Analysis	-
PointNet++	90.7	PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space	-
DSPoint	93.5	DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion	-
GBNet	93.8	Geometric Back-projection Network for Point Cloud Classification	-
PointNet + SageMix	90.3	SageMix: Saliency-Guided Mixup for Point Clouds	-
Point-M2AE	94.0	Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training	-
Point-PN	93.8	Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis	-
APES (local-based downsample)	93.5	Attention-based Point Cloud Edge Sampling	-
PointGPT	94.9	-	-
PointNeXt	94.0	PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies	-
Perceiver	-	Perceiver: General Perception with Iterative Attention	-
GDANet	93.8	Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud	-
VRN (single view)	-	Generative and Discriminative Voxel Modeling with Convolutional Neural Networks	-

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