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 21–30 of 1723 papers

TitleStatusHype
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations—0
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
Unified Representation Space for 3D Visual Grounding—0
SceneAware: Scene-Constrained Pedestrian Trajectory Prediction with LLM-Guided WalkabilityCode0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis—0
SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields—0
Robust Visual Localization via Semantic-Guided Multi-Scale Transformer—0
Show:102550
← PrevPage 3 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