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 1121–1130 of 1723 papers

TitleStatusHype
Learning 3D Robotics Perception using Inductive Priors—0
Learning 3D Scene Priors with 2D Supervision—0
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions—0
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation—0
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
Learning-based Relational Object Matching Across Views—0
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation—0
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes—0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
Learning Direct Optimization for Scene Understanding—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