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 1641–1650 of 1723 papers

TitleStatusHype
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations—0
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction—0
High-Accuracy Facial Depth Models derived from 3D Synthetic Data—0
Highway Driving Dataset for Semantic Video Segmentation—0
Factor Graph based 3D Multi-Object Tracking in Point Clouds—0
HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding—0
Factored Neural Representation for Scene Understanding—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—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