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SOTA
Semantic Segmentation
Semantic Segmentation On Scannetv2
Semantic Segmentation On Scannetv2
Métriques
Mean IoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Mean IoU
Paper Title
Repository
PSPNet
47.5%
Pyramid Scene Parsing Network
CMX
61.3%
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
AdapNet++
50.3
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
ENet
37.6%
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
ScanNet (2d proj)
33.0%
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
SSMA
57.7
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
Floors are Flat
-
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
RFBNet
59.2%
RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation
-
EMSAFormer
56.4%
Efficient Multi-Task Scene Analysis with RGB-D Transformers
EMSANet (2x ResNet-34 NBt1D, PanopticNDT version)
60.0%
PanopticNDT: Efficient and Robust Panoptic Mapping
3DMV (2d proj)
49.8%
3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
MSeg1080_RVC
48.5%
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
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