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 351–360 of 1723 papers

Title	Date	Tasks	Status	Hype
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions	Oct 4, 2022	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection	Dec 5, 2023	3D Object DetectionDenoising	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
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks	Feb 17, 2023	DeblurringDeep Learning	CodeCode Available	1
Deep learning for radar data exploitation of autonomous vehicle	Mar 15, 2022	Autonomous DrivingDeep Learning	CodeCode Available	1
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding	Nov 9, 2015	Decision MakingDecoder	CodeCode Available	1
Multi3DRefer: Grounding Text Description to Multiple 3D Objects	Sep 11, 2023	3D visual groundingContrastive Learning	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
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP	Jan 12, 2023	3D Semantic SegmentationContrastive Learning	CodeCode Available	1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation	Dec 22, 2021	Common Sense ReasoningQuestion Answering	CodeCode Available	1

Show:10 25 50

← PrevPage 36 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