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Semantic Segmentation On Nyu Depth V2

评估指标

Mean IoU

评测结果

各个模型在此基准测试上的表现结果

模型名称
Mean IoU
Paper TitleRepository
DynMM (ResNet-50)51.0%Dynamic Multimodal Fusion
GeminiFusion (Swin-Large)60.9GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer
CMX (B2)54.4%CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
LS-DeconvNet45.9%Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation-
ESANet (R18-NBt1D )48.17Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis-
3DGNN43.1%3D Graph Neural Networks for RGBD Semantic Segmentation
AsymFormer55.3%AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation
AsymFusion (ResNet-152)51.2%Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
SwinMTL58.14%SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images
OmniVec263.6OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning-
MMAF-Net-15244.8%Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images-
GeminiFusion (MiT-B3)56.8GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer
Cross-stitch19.3%Cross-stitch Networks for Multi-task Learning
HN-network33.49%RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training-
CMNeXt (B4)56.9%Delivering Arbitrary-Modal Semantic Segmentation
CFN47.7%Cascaded Feature Network for Semantic Segmentation of RGB-D Images-
NDDR-CNN43.3%NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
ACNet48.3%ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
Malleable 2.5D (ResNet-101)50.9%Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing
SGACNet (R34-NBt1D)49.4%Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
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