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 971–980 of 1723 papers

Title	Date	Tasks	Status	Hype
Uncertainty-aware Panoptic Segmentation	Jun 29, 2022	Panoptic SegmentationScene Understanding	CodeCode Available	1
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving	Jun 27, 2022	Autonomous DrivingDepth Estimation	CodeCode Available	1
IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments	Jun 27, 2022	Autonomous VehiclesScene Segmentation	CodeCode Available	1
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings	Jun 24, 2022	Scene UnderstandingSemantic Segmentation	CodeCode Available	0
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning	Jun 21, 2022	Contrastive LearningDomain Generalization	CodeCode Available	1
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding	Jun 21, 2022	ClusteringObject Discovery	CodeCode Available	0
A Dynamic Data Driven Approach for Explainable Scene Understanding	Jun 18, 2022	Autonomous DrivingScene Understanding	—Unverified	0
On Efficient Real-Time Semantic Segmentation: A Survey	Jun 17, 2022	GPUobject-detection	—Unverified	0
Waymo Open Dataset: Panoramic Video Panoptic Segmentation	Jun 15, 2022	3D Multi-Object TrackingAutonomous Driving	—Unverified	0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth	Jun 13, 2022	Objectobject-detection	—Unverified	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