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

Showing 201250 of 1723 papers

TitleStatusHype
Dense Audio-Visual Event Localization under Cross-Modal Consistency and Multi-Temporal Granularity CollaborationCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
Egocentric Scene Understanding via Multimodal Spatial RectifierCode1
Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingCode1
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving EnvironmentsCode1
Efficient Multi-Task RGB-D Scene Analysis for Indoor EnvironmentsCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
Bootstraping Clustering of Gaussians for View-consistent 3D Scene UnderstandingCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
A Data-Centric Revisit of Pre-Trained Vision Models for Robot LearningCode1
Boundary-induced and scene-aggregated network for monocular depth predictionCode1
Learning and Reasoning with the Graph Structure Representation in Robotic SurgeryCode1
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal EstimationCode1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based OptimizationCode1
Event-aided Semantic Scene CompletionCode1
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous DrivingCode1
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene ContextsCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Explainable Object-induced Action Decision for Autonomous VehiclesCode1
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed ImagesCode1
F-ViTA: Foundation Model Guided Visible to Thermal TranslationCode1
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene UnderstandingCode1
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3DCode1
AVSegFormer: Audio-Visual Segmentation with TransformerCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal EstimationCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
Monte Carlo Scene Search for 3D Scene UnderstandingCode1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier ConvolutionsCode1
From General to Specific: Informative Scene Graph Generation via Balance AdjustmentCode1
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree NetworksCode1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCode1
Deep learning for radar data exploitation of autonomous vehicleCode1
Instance-wise Occlusion and Depth Orders in Natural ScenesCode1
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
All-Day Multi-Camera Multi-Target TrackingCode1
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion FramesCode1
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic SegmentationCode1
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic FieldsCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44Unverified
2Team VGAI (TCS Research)OMQ0.37Unverified
3Demo_semantic_SLAMOMQ0.11Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35Unverified