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المنصة
الرئيسية
SOTA
التمييز الدلالي
Semantic Segmentation On Scannet
Semantic Segmentation On Scannet
المقاييس
test mIoU
val mIoU
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
test mIoU
val mIoU
Paper Title
PTv3 ARKit LabelMaker
79.8
79.1
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
DITR
79.7
80.5
DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
PTv3 + PPT
79.4
78.6
Point Transformer V3: Simpler, Faster, Stronger
PonderV2 + SparseUNet
78.5
77.0
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Mix3D
78.1
73.6
Mix3D: Out-of-Context Data Augmentation for 3D Scenes
Swin3D-L
77.9
77.5
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
OctFormer
76.6
75.7
OctFormer: Octree-based Transformers for 3D Point Clouds
PPT + SparseUNet
76.6
76.4
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
PointHR
76.6
75.4
PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation
O-CNN
76.2
74.0
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
OA-CNNs
75.6
76.1
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
LSK3DNet
75.5
75.7
LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
PTv2
75.2
75.4
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
BPNet
74.9
73.9
Bidirectional Projection Network for Cross Dimension Scene Understanding
VMVF
74.6
-
Virtual Multi-view Fusion for 3D Semantic Segmentation
ODIN
74.4
77.8
ODIN: A Single Model for 2D and 3D Segmentation
EQ-Net
74.3
75.3
A Unified Query-based Paradigm for Point Cloud Understanding
StratifiedFormer
73.7
74.3
Stratified Transformer for 3D Point Cloud Segmentation
MinkowskiNet
73.4
72.2
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
SparseConvNet
72.5
69.3
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
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Semantic Segmentation On Scannet | SOTA | HyperAI