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 1551–1560 of 1723 papers

TitleStatusHype
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds—0
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model—0
General-Purpose Deep Point Cloud Feature ExtractorCode0
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey—0
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition—0
Structured Label Inference for Visual UnderstandingCode0
Scenarios: A New Representation for Complex Scene Understanding—0
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning AbstractionsCode0
Recognizing Material Properties from Images—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—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