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 591–600 of 1723 papers

TitleStatusHype
Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments—0
NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Endo-TTAP: Robust Endoscopic Tissue Tracking via Multi-Facet Guided Attention and Hybrid Flow-point Supervision—0
Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
OpenLex3D: A New Evaluation Benchmark for Open-Vocabulary 3D Scene Representations—0
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving—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