HyperAI초신경

3D Part Segmentation On Shapenet Part

평가 지표

Class Average IoU
Instance Average IoU

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Class Average IoU
Instance Average IoU
Paper TitleRepository
InterpCNN84.086.3Interpolated Convolutional Networks for 3D Point Cloud Understanding-
Point Voxel Transformer-86.5PVT: Point-Voxel Transformer for Point Cloud Learning
Point-JEPA85.8±0.1-Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud-
SSCNN82.084.7SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation-
Point Cloud Transformer-86.4PCT: Point cloud transformer
DensePoint84.286.4DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
CurveNet-86.8Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
3D-JEPA86.4184.933D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning-
KPConv85.186.4KPConv: Flexible and Deformable Convolution for Point Clouds
GeomGCNN-89.1Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks-
PartNet84.1-PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation-
point2vec84.686.3Point2Vec for Self-Supervised Representation Learning on Point Clouds
RS-CNN-86.2Relation-Shape Convolutional Neural Network for Point Cloud Analysis
SGPN-85.8SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
P2Sequence-85.2Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network-
PointNet-83.7PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointGPT84.886.6--
PointGrid82.286.4PointGrid: A Deep Network for 3D Shape Understanding
DeltaConv (U-ResNet)-86.9DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
ConvPoint83.485.8ConvPoint: Continuous Convolutions for Point Cloud Processing
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