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 1011–1020 of 1723 papers

TitleStatusHype
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
Deep Semantic Segmentation of Natural and Medical Images: A Review—0
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding—0
OmniIndoor3D: Comprehensive Indoor 3D Reconstruction—0
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
Deep Scene Text Detection with Connected Component Proposals—0
On Efficient Real-Time Semantic Segmentation: A Survey—0
Urban Scene Diffusion through Semantic Occupancy Map—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