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SOTA
Semantic Segmentation
Semantic Segmentation On Scannet
Semantic Segmentation On Scannet
Metrics
test mIoU
val mIoU
Results
Performance results of various models on this benchmark
Columns
Model Name
test mIoU
val mIoU
Paper Title
Repository
LSK3DNet
75.5
75.7
LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
-
MSC + SparseUNet
-
75.5
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
-
FCPN
44.7
-
Fully-Convolutional Point Networks for Large-Scale Point Clouds
-
Tangent Convolutions
44.2
-
Tangent Convolutions for Dense Prediction in 3D
-
BFANet
-
78.0
BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis
-
PTv2
75.2
75.4
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
-
PointCNN
45.8
-
PointCNN: Convolution On X-Transformed Points
Serialized Piont Mamba
-
76.8
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model
-
TextureNet
56.6
-
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
-
FG-Net
69.0
-
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
-
OctFormer
76.6
75.7
OctFormer: Octree-based Transformers for 3D Point Clouds
-
3DMV
48.4
-
3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
-
PointNet++
33.9
53.5
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
-
KPConvX-L
-
76.3
KPConvX: Modernizing Kernel Point Convolution with Kernel Attention
-
BPNet
74.9
73.9
Bidirectional Projection Network for Cross Dimension Scene Understanding
-
StratifiedFormer
73.7
74.3
Stratified Transformer for 3D Point Cloud Segmentation
-
PPT + SparseUNet
76.6
76.4
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
-
OneFormer3D
-
76.6
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
-
PTv3 ARKit LabelMaker
79.8
79.1
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
-
AVS-Net
-
76.1
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
-
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