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 876–900 of 1723 papers

TitleStatusHype
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
Neural Radiance Field CodebooksCode0
Plausible Uncertainties for Human Pose Regression—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation—0
Self-Supervised Object Detection from Egocentric Videos—0
Uni-3D: A Universal Model for Panoptic 3D Scene ReconstructionCode1
Seeing With Sound: Long-range Acoustic Beamforming for Multimodal Scene Understanding—0
Learning Geometric-Aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs—0
Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation—0
PeakConv: Learning Peak Receptive Field for Radar Semantic SegmentationCode1
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification—0
PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image TranslationCode1
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene UnderstandingCode0
METEOR Guided Divergence for Video CaptioningCode0
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency—0
Panoptic Lifting for 3D Scene Understanding with Neural FieldsCode2
Learning Object-level Point Augmentor for Semi-supervised 3D Object DetectionCode1
Lightweight integration of 3D features to improve 2D image segmentationCode0
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation—0
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding—0
Towards Holistic Surgical Scene UnderstandingCode1
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous DrivingCode1
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data—0
Framework for 2D Ad placements in LinearTV—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