HyperAI

Semantic Segmentation On S3Dis Area5

Metrics

Number of params
mAcc
mIoU
oAcc

Results

Performance results of various models on this benchmark

Model Name
Number of params
mAcc
mIoU
oAcc
Paper TitleRepository
SPoTrN/A76.470.890.7Self-positioning Point-based Transformer for Point Cloud Understanding
PointVector-XL-78.172.391PointVector: A Vector Representation In Point Cloud Analysis
DITR--74.1---
KPConv14.1M72.867.1-KPConv: Flexible and Deformable Convolution for Point Clouds
PointMixer6.5M77.471.4-PointMixer: MLP-Mixer for Point Cloud Understanding
TangentConvN/A62.2--Tangent Convolutions for Dense Prediction in 3D
SSP+SPG290K68.261.787.9Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
DPCN/A-61.28-Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
HPEINN/A68.361.8587.18Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation-
SegCloudN/A57.448.9-SEGCloud: Semantic Segmentation of 3D Point Clouds-
WindowNorm+PointTransformerN/A77.971.491.1Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities
SuperCluster0.21-68.1-Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
Swin3D-LN/A80.574.592.7Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
PointNetN/A-41.1-Point Transformer
SPG(PTv2)-79.573.391.9Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation
Pamba--73.5-Pamba: Enhancing Global Interaction in Point Clouds via State Space Model-
ConDaFormer-78.973.592.4ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
Serialized Piont Mamba--70.6-Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model-
SCF-NetN/A71.863.787.2SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
Superpoint Transformer212K77.368.989.5Efficient 3D Semantic Segmentation with Superpoint Transformer
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