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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 91–100 of 1723 papers

Title	Date	Tasks	Status	Hype
Embracing Diffraction: A Paradigm Shift in Wireless Sensing and Communication	May 2, 2025	Scene Understanding	—Unverified	0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving	Apr 30, 2025	Autonomous DrivingDecision Making	—Unverified	0
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics	Apr 30, 2025	In-Context LearningObject	CodeCode Available	1
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding	Apr 28, 2025	3D Semantic SegmentationContrastive Learning	—Unverified	0
Category-Level and Open-Set Object Pose Estimation for Robotics	Apr 28, 2025	6D Pose Estimation6D Pose Estimation using RGB	—Unverified	0
TraveLLaMA: Facilitating Multi-modal Large Language Models to Understand Urban Scenes and Provide Travel Assistance	Apr 23, 2025	Question AnsweringScene Understanding	—Unverified	0
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends	Apr 21, 2025	Adversarial RobustnessDecision Making	—Unverified	0
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding	Apr 20, 2025	Autonomous DrivingImage Captioning	CodeCode Available	0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation	Apr 20, 2025	AttributeForeground Segmentation	—Unverified	0
Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding	Apr 18, 2025	Deep LearningPoint Cloud Completion	CodeCode Available	0

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All datasets Semantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K val Semantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ACRV Baseline	OMQ	0.44	—	Unverified
2	Team VGAI (TCS Research)	OMQ	0.37	—	Unverified
3	Demo_semantic_SLAM	OMQ	0.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CPN(ResNet-101)	Mean IoU	46.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ACRV Baseline	OMQ	0.35	—	Unverified