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 1151–1200 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
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding—0
Category-Level and Open-Set Object Pose Estimation for Robotics—0
Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV—0
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
RailSem19: A Dataset for Semantic Rail Scene Understanding—0
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds—0
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning—0
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry—0
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration—0
RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
REACT: Recognize Every Action Everywhere All At Once—0
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Real time backbone for semantic segmentation—0
Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL)—0
Real-Time Semantic Stereo Matching—0
Reasoning About Physical Interactions with Object-Centric Models—0
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning—0
Reasoning with shapes: profiting cognitive susceptibilities to infer linear mapping transformations between shapes—0
Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation—0
Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments—0
Recognizing Material Properties from Images—0
Reconstructing Animals and the Wild—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