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 226–250 of 1723 papers

TitleStatusHype
Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Explainable Object-induced Action Decision for Autonomous VehiclesCode1
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household RoboticsCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed ImagesCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene UnderstandingCode1
MassMIND: Massachusetts Maritime INfrared DatasetCode1
MGNet: Monocular Geometric Scene Understanding for Autonomous DrivingCode1
Microsoft COCO: Common Objects in ContextCode1
Advances in Deep Concealed Scene UnderstandingCode1
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
All-Day Multi-Camera Multi-Target TrackingCode1
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce ModelCode1
Estimating Generic 3D Room Structures from 2D AnnotationsCode1
Monte Carlo Scene Search for 3D Scene UnderstandingCode1
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based DecodersCode1
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic SegmentationCode1
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal EstimationCode1
Event-aided Semantic Scene CompletionCode1
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor SetupsCode1
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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