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 226–250 of 1723 papers

TitleStatusHype
Efficient Interactive 3D Multi-Object Removal—0
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding—0
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and GenerationCode3
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Neural Radiance Fields for the Real World: A Survey—0
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
Dynamic Scene Understanding from Vision-Language Representations—0
A Survey of World Models for Autonomous DrivingCode1
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate RepresentationCode0
YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks—0
Embodied Scene Understanding for Vision Language Models via MetaVQA—0
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene UnderstandingCode1
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models—0
Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving—0
Self-Supervised Partial Cycle-Consistency for Multi-View MatchingCode0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation DataCode0
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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