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 921–930 of 1723 papers

TitleStatusHype
Dynamic Scene Understanding from Vision-Language Representations—0
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding—0
EarthNets: Empowering AI in Earth Observation—0
Efficient 3D Instance Mapping and Localization with Neural Fields—0
Efficient Interactive 3D Multi-Object Removal—0
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
Egocentric Activity Recognition and Localization on a 3D 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