Scene Segmentation
122 papers with code • 5 benchmarks • 7 datasets
Scene segmentation is the task of splitting a scene into its various object components.
Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.
Benchmarks
These leaderboards are used to track progress in Scene Segmentation
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Libraries
Use these libraries to find Scene Segmentation models and implementations
ScanNet
NYUv2
SUN RGB-D
UAVid
StreetHazards
Mila Simulated Floods
Berkeley DeepDrive Video
Latest papers
Uni-3D: A Universal Model for Panoptic 3D Scene Reconstruction
mlpc-ucsd/uni-3d
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ICCV 2023
Performing holistic 3D scene understanding from a single-view observation, involving generating instance shapes and 3D scene segmentation, is a long-standing challenge.
Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds
To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism.
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
yyliu01/rpl
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ICCV 2023
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes.
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation
leungtsang/cpcdr
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For unsupervised semantic segmentation of urban scenes, our method surpasses the previous state-of-the-art baseline by +7. 14% in mIoU on Cityscapes and +6. 65% on KITTI.
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
ganlumomo/thermal-uda-attention
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This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network.
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Introducing Intermediate Domains for Effective Self-Training during Test-Time
mariodoebler/test-time-adaptation
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In this work, we address two problems that exist when applying self-training in the setting of test-time adaptation.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art.
Self-attention on Multi-Shifted Windows for Scene Segmentation
yutao1008/mswin
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Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
We blend these two predictions into a hybrid anomaly score which allows dense open-set recognition on large natural images.
