3D Part Segmentation On Shapenet Part

المقاييس

Class Average IoU

Instance Average IoU

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	Class Average IoU	Instance Average IoU	Paper Title	Repository
InterpCNN	84.0	86.3	Interpolated Convolutional Networks for 3D Point Cloud Understanding	-
Point Voxel Transformer	-	86.5	PVT: Point-Voxel Transformer for Point Cloud Learning
Point-JEPA	85.8±0.1	-	Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud	-
SSCNN	82.0	84.7	SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation	-
Point Cloud Transformer	-	86.4	PCT: Point cloud transformer
DensePoint	84.2	86.4	DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
CurveNet	-	86.8	Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
3D-JEPA	86.41	84.93	3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning	-
KPConv	85.1	86.4	KPConv: Flexible and Deformable Convolution for Point Clouds
GeomGCNN	-	89.1	Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks	-
PartNet	84.1	-	PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation	-
point2vec	84.6	86.3	Point2Vec for Self-Supervised Representation Learning on Point Clouds
RS-CNN	-	86.2	Relation-Shape Convolutional Neural Network for Point Cloud Analysis
SGPN	-	85.8	SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
P2Sequence	-	85.2	Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network	-
PointNet	-	83.7	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointGPT	84.8	86.6	-	-
PointGrid	82.2	86.4	PointGrid: A Deep Network for 3D Shape Understanding
DeltaConv (U-ResNet)	-	86.9	DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
ConvPoint	83.4	85.8	ConvPoint: Continuous Convolutions for Point Cloud Processing

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