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세마틱 세그멘테이션
Semantic Segmentation On Scannet
Semantic Segmentation On Scannet
평가 지표
test mIoU
val mIoU
평가 결과
이 벤치마크에서 각 모델의 성능 결과
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모델 이름
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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