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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1581–1590 of 1723 papers

TitleStatusHype
GaussianBeV: 3D Gaussian Representation meets Perception Models for BeV Segmentation—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
Framework for 2D Ad placements in LinearTV—0
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting—0
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data—0
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization—0
FMLGS: Fast Multilevel Language Embedded Gaussians for Part-level Interactive Agents—0
Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin-based Scene Representation—0
General-Purpose Aerial Intelligent Agents Empowered by Large Language Models—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Show:102550
← PrevPage 159 of 173Next →
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