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 291–300 of 1723 papers

Title	Date	Tasks	Status	Hype
SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion	Sep 1, 2022	Depth CompletionScene Understanding	CodeCode Available	1
Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation	Aug 9, 2022	3D Instance Segmentation3D Part Segmentation	CodeCode Available	1
TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation	Aug 3, 2022	Answer GenerationQuestion-Answer-Generation	CodeCode Available	1
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud	Jul 28, 2022	Scene Understanding	CodeCode Available	1
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving	Jul 26, 2022	3D Semantic SegmentationAutonomous Driving	CodeCode Available	1
Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models	Jul 23, 2022	Scene Understanding	CodeCode Available	1
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization	Jul 22, 2022	3D Instance Segmentation3D Object Detection	CodeCode Available	1
Egocentric Scene Understanding via Multimodal Spatial Rectifier	Jul 14, 2022	Scene UnderstandingSurface Normal Estimation	CodeCode Available	1
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments	Jul 10, 2022	Instance SegmentationPanoptic Segmentation	CodeCode Available	1
MCTS with Refinement for Proposals Selection Games in Scene Understanding	Jul 7, 2022	Scene Understanding	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