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 301–310 of 1723 papers

Title	Date	Tasks	Status	Hype
GFF: Gated Fully Fusion for Semantic Segmentation	Apr 3, 2019	Scene ParsingScene Understanding	CodeCode Available	1
Global Aggregation then Local Distribution in Fully Convolutional Networks	Sep 16, 2019	Instance Segmentationobject-detection	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
4D Panoptic LiDAR Segmentation	Feb 24, 2021	4D Panoptic SegmentationBenchmarking	CodeCode Available	1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection	Jan 1, 2021	Common Sense ReasoningGraph Generation	CodeCode Available	1
Group Contextual Encoding for 3D Point Clouds	Dec 1, 2020	Scene Understanding	CodeCode Available	1
A Two-Stage Masked Autoencoder Based Network for Indoor Depth Completion	Jun 14, 2024	3D ReconstructionAutonomous Driving	CodeCode Available	1
Context Prior for Scene Segmentation	Apr 3, 2020	Scene SegmentationScene Understanding	CodeCode Available	1
CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments	May 23, 2024	Pose EstimationScene Understanding	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

Show:10 25 50

← PrevPage 31 of 173Next →

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