Semantic Segmentation On S3Dis

Métriques

Mean IoU

Number of params

oAcc

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Mean IoU	Number of params	oAcc	Paper Title	Repository
A-CNN	62.9	N/A	87.3	A-CNN: Annularly Convolutional Neural Networks on Point Clouds	-
DeepViewAgg	74.7	41.2M	90.1	Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation	-
RandLA-Net	-	1.2M	87.1	RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds	-
MinkowskiNet	65.4	37.9M	-	4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks	-
MuGNet	69.8	N/A	88.5	MuGNet: Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
EQ-Net	77.5	N/A	-	A Unified Query-based Paradigm for Point Cloud Understanding	-
Feature Geometric Net (FG-Net)	70.8	N/A	88.2	FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling	-
Swin3D-L	79.8	N/A	92.4	Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding	-
JSENet	67.7	N/A	-	JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds	-
BIM-Net	-	-	-	Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation
PointCNN	65.4	N/A	88.1	A-CNN: Annularly Convolutional Neural Networks on Point Clouds	-
PointASNL	68.7	N/A	88.8	PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling	-
KPConv	70.6	14.1M	-	KPConv: Flexible and Deformable Convolution for Point Clouds	-
PointCNN	65.4	N/A	-	Point Transformer	-
BAAF-Net	72.2	N/A	88.9	Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion	-
PPT + SparseUNet	78.1	N/A	92.2	Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training	-
JSNet	61.7	N/A	88.7	JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds	-
ShellNet	66.8	N/A	-	ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics	-
SPGraph	62.1	N/A	85.5	A-CNN: Annularly Convolutional Neural Networks on Point Clouds	-
3P-RNN	56.3	N/A	86.9	A-CNN: Annularly Convolutional Neural Networks on Point Clouds	-

0 of 54 row(s) selected.