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
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic SegmentationCode1
Human-centric Scene Understanding for 3D Large-scale ScenariosCode1
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic SegmentationCode1
Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual ImpairmentsCode1
The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context DatasetCode1
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
Towards accurate instance segmentation in large-scale LiDAR point cloudsCode1
AVSegFormer: Audio-Visual Segmentation with TransformerCode1
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV FusionCode1
Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape PriorCode1
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic SegmentationCode1
Estimating Generic 3D Room Structures from 2D AnnotationsCode1
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic UnderstandingCode1
Towards Label-free Scene Understanding by Vision Foundation ModelsCode1
Towards In-context Scene UnderstandingCode1
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
Multi-Scale Attention for Audio Question AnsweringCode1
Generating Visual Spatial Description via Holistic 3D Scene UnderstandingCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and BeyondCode1
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric VoxelizationCode1
A Review of Panoptic Segmentation for Mobile Mapping Point CloudsCode1
RGB-D Indiscernible Object Counting in Underwater ScenesCode1
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
Advances in Deep Concealed Scene UnderstandingCode1
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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