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 231–240 of 1723 papers

TitleStatusHype
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and GenerationCode3
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Neural Radiance Fields for the Real World: A Survey—0
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
Dynamic Scene Understanding from Vision-Language Representations—0
A Survey of World Models for Autonomous DrivingCode1
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate RepresentationCode0
YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks—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