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 1401–1450 of 1723 papers

TitleStatusHype
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation—0
Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAM—0
Quantifying the synthetic and real domain gap in aerial scene understanding—0
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding—0
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding—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
REACT: Recognize Every Action Everywhere All At Once—0
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation—0
Real time backbone for semantic segmentation—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
Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications—0
Referring Self-supervised Learning on 3D Point Cloud—0
RefineCap: Concept-Aware Refinement for Image Captioning—0
Relationship Proposal Networks—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision—0
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction—0
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
VrR-VG: Refocusing Visually-Relevant Relationships—0
Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding—0
Review on Panoramic Imaging and Its Applications in Scene Understanding—0
Right Side Up? Disentangling Orientation Understanding in MLLMs with Fine-grained Multi-axis Perception Tasks—0
Road Rage Reasoning with Vision-language Models (VLMs): Task Definition and Evaluation Dataset—0
Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets—0
RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics—0
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion—0
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Robust deep learning-based semantic organ segmentation in hyperspectral images—0
Robust Multi-Modal Image Stitching for Improved 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