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 1481–1490 of 1723 papers

Title	Date	Tasks	Status
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular Video	Jan 1, 2024	3D Panoptic Segmentation3D Reconstruction	CodeCode Available
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes	Aug 18, 2021	Camera CalibrationDepth Estimation	CodeCode Available
P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation	Oct 23, 2023	Autonomous DrivingDecoder	CodeCode Available
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation	Nov 30, 2022	Graph GenerationImage Generation	CodeCode Available
Pose-aware Multi-level Feature Network for Human Object Interaction Detection	Sep 18, 2019	Human-Object Interaction DetectionObject	CodeCode Available
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies	Dec 31, 2024	3DGS3D Semantic Segmentation	CodeCode Available
Dilated Residual Networks	May 28, 2017	ClassificationGeneral Classification	CodeCode Available
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation	Sep 24, 2018	Autonomous DrivingReal-Time Semantic Segmentation	CodeCode Available
OVeNet: Offset Vector Network for Semantic Segmentation	Mar 25, 2023	Optical Character Recognition (OCR)Scene Understanding	CodeCode Available
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images	May 8, 2019	Domain AdaptationManagement	CodeCode Available

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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