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 171–180 of 1723 papers

Title	Date	Tasks	Status	Hype
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images	Feb 16, 2021	Decision MakingScene Understanding	CodeCode Available	1
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments	Jul 10, 2022	Instance SegmentationPanoptic Segmentation	CodeCode Available	1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation	Apr 16, 2024	3D Semantic SegmentationManagement	CodeCode Available	1
Egocentric Scene Understanding via Multimodal Spatial Rectifier	Jul 14, 2022	Scene UnderstandingSurface Normal Estimation	CodeCode Available	1
AVSegFormer: Audio-Visual Segmentation with Transformer	Jul 3, 2023	DecoderScene Understanding	CodeCode Available	1
Dynamic Graph Message Passing Networks for Visual Recognition	Sep 20, 2022	image-classificationImage Classification	CodeCode Available	1
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding	Nov 9, 2015	Decision MakingDecoder	CodeCode Available	1
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery	Jan 20, 2025	Language ModelingLanguage Modelling	CodeCode Available	1
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation	Sep 20, 2021	DecoderPrediction	CodeCode Available	1
Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering	Jul 30, 2024	Inverse RenderingNeRF	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