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 1561–1570 of 1723 papers

TitleStatusHype
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding—0
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Fusion Based Holistic Road Scene Understanding—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation—0
Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences—0
FroDO: From Detections to 3D Objects—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