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 Title	Repository
SPoTr	N/A	76.4	70.8	90.7	Self-positioning Point-based Transformer for Point Cloud Understanding	-
PointVector-XL	-	78.1	72.3	91	PointVector: A Vector Representation In Point Cloud Analysis	-
DITR	-	-	74.1	-	DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation	-
KPConv	14.1M	72.8	67.1	-	KPConv: Flexible and Deformable Convolution for Point Clouds	-
PointMixer	6.5M	77.4	71.4	-	PointMixer: MLP-Mixer for Point Cloud Understanding	-
TangentConv	N/A	62.2	-	-	Tangent Convolutions for Dense Prediction in 3D	-
SSP+SPG	290K	68.2	61.7	87.9	Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning	-
DPC	N/A	-	61.28	-	Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds	-
HPEIN	N/A	68.3	61.85	87.18	Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation	-
SegCloud	N/A	57.4	48.9	-	SEGCloud: Semantic Segmentation of 3D Point Clouds	-
WindowNorm+PointTransformer	N/A	77.9	71.4	91.1	Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities	-
SuperCluster	0.21	-	68.1	-	Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering	-
Swin3D-L	N/A	80.5	74.5	92.7	Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding	-
PointNet	N/A	-	41.1	-	Point Transformer	-
SPG(PTv2)	-	79.5	73.3	91.9	Subspace 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	-	78.9	73.5	92.4	ConDaFormer: 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-Net	N/A	71.8	63.7	87.2	SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
Superpoint Transformer	212K	77.3	68.9	89.5	Efficient 3D Semantic Segmentation with Superpoint Transformer	-

0 of 60 row(s) selected.