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 1401–1425 of 1723 papers

TitleStatusHype
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense—0
Texture Underfitting for Domain Adaptation—0
Dynamic Graph Message Passing NetworksCode1
Rotation Invariant Convolutions for 3D Point Clouds Deep LearningCode0
RIO: 3D Object Instance Re-Localization in Changing Indoor EnvironmentsCode0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
VideoNavQA: Bridging the Gap between Visual and Embodied Question AnsweringCode1
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes—0
Object as Distribution—0
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational InteractantsCode0
SDNet: Semantically Guided Depth Estimation Network—0
U4D: Unsupervised 4D Dynamic Scene Understanding—0
Temporally Consistent Horizon LinesCode0
M3D-RPN: Monocular 3D Region Proposal Network for Object DetectionCode1
Structure-Aware Residual Pyramid Network for Monocular Depth EstimationCode0
Preferences Prediction using a Gallery of Mobile Device based on Scene Recognition and Object DetectionCode0
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation NetworkCode1
CaDIS: Cataract Dataset for Image Segmentation—0
Loss Switching Fusion with Similarity Search for Video ClassificationCode0
Active Scene Understanding via Online Semantic Reconstruction—0
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes—0
RailSem19: A Dataset for Semantic Rail Scene Understanding—0
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding—0
A New Ratio Image Based CNN Algorithm For SAR Despeckling—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