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 1351–1375 of 1723 papers

TitleStatusHype
Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics—0
Semantic Augmented Reality Environment with Material-Aware Physical Interactions—0
Semantic-aware Transmission for Robust Point Cloud Classification—0
Semantic Dense Reconstruction with Consistent Scene Segments—0
Semantic Detection of Potential Wind-borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Semantic Foggy Scene Understanding with Synthetic Data—0
Classification of Single-View Object Point Clouds—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction—0
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Benchmarking Vision Language Models for Cultural Understanding—0
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation—0
BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery—0
Semantic segmentation of surgical hyperspectral images under geometric domain shifts—0
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics—0
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry—0
VLP: Vision Language Planning for Autonomous Driving—0
SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields—0
3D Object Aided Self-Supervised Monocular Depth Estimation—0
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification—0
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense 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