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 321–330 of 1723 papers

Title	Date	Tasks	Status	Hype
AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans	Mar 24, 2024	3D Instance SegmentationInstance Segmentation	CodeCode Available	1
Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding	Mar 20, 2025	Scene Understanding	CodeCode Available	1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning	May 31, 2022	Common Sense ReasoningGraph Generation	CodeCode Available	1
Language-Assisted 3D Feature Learning for Semantic Scene Understanding	Nov 25, 2022	DescriptiveInstance Segmentation	CodeCode Available	1
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups	Jan 12, 2021	Scene Understanding	CodeCode Available	1
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts	Dec 16, 2020	3D Semantic SegmentationInstance Segmentation	CodeCode Available	1
CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes	Jul 1, 2024	Autonomous VehiclesImage Segmentation	CodeCode Available	1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding	Jan 5, 2019	Domain AdaptationScene Understanding	CodeCode Available	1
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation	Oct 30, 2020	Instance SegmentationPanoptic Segmentation	CodeCode Available	1
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving	May 13, 2025	3D visual groundingAutonomous Driving	CodeCode Available	1

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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