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 741–750 of 1723 papers

TitleStatusHype
ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail—0
EvSegSNN: Neuromorphic Semantic Segmentation for Event Data—0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond—0
Event fields: Capturing light fields at high speed, resolution, and dynamic range—0
Category-Level and Open-Set Object Pose Estimation for Robotics—0
Evaluation of Multimodal Semantic Segmentation using RGB-D Data—0
Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL)—0
A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding—0
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets—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