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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1175 of 1723 papers

TitleStatusHype
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling—0
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos—0
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding—0
Predicting Reaction Time to Comprehend Scenes with Foveated Scene Understanding Maps—0
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving—0
Prediction of Scene Plausibility—0
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network—0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning—0
PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario—0
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation—0
Probabilistic Future Prediction for Video Scene Understanding—0
ChatSplat: 3D Conversational Gaussian Splatting—0
ChatBEV: A Visual Language Model that Understands BEV Maps—0
ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation—0
Prospective Role of Foundation Models in Advancing Autonomous Vehicles—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
PSDR-Room: Single Photo to Scene using Differentiable Rendering—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation—0
Challenges for Monocular 6D Object Pose Estimation in Robotics—0
Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAM—0
Quantifying the synthetic and real domain gap in aerial scene understanding—0
Vision-Language Embodiment for Monocular Depth Estimation—0
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding—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