SOTAVerified

Scene Understanding

Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.

Papers

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Showing 1221–1230 of 1723 papers

TitleStatusHype
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
3D Shape Augmentation with Content-Aware Shape Resizing—0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
VrR-VG: Refocusing Visually-Relevant Relationships—0
Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding—0
Review on Panoramic Imaging and Its Applications in Scene Understanding—0
3D Scene Understanding at Urban Intersection using Stereo Vision and Digital Map—0
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified