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 401–425 of 1723 papers

TitleStatusHype
LoLI-Street: Benchmarking Low-Light Image Enhancement and BeyondCode1
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene UnderstandingCode1
Dynamic Scene Understanding through Object-Centric Voxelization and Neural RenderingCode1
SemSegDepth: A Combined Model for Semantic Segmentation and Depth CompletionCode1
Online panoptic 3D reconstruction as a Linear Assignment ProblemCode1
Dual-Hybrid Attention Network for Specular Highlight RemovalCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
Grounded Situation Recognition with TransformersCode1
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving EnvironmentsCode1
Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR SequencesCode1
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object DetectionCode1
MassMIND: Massachusetts Maritime INfrared DatasetCode1
Dynamic Graph Message Passing NetworksCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense KnowledgeCode1
Monte Carlo Scene Search for 3D Scene UnderstandingCode1
You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene UnderstandingCode1
MGNet: Monocular Geometric Scene Understanding for Autonomous DrivingCode1
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D DataCode1
Mitigating Trade-off: Stream and Query-guided Aggregation for Efficient and Effective 3D Occupancy PredictionCode1
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene UnderstandingCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
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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