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 1341–1350 of 1723 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks on Monocular Depth Estimation	Mar 23, 2020	Autonomous DrivingDepth Estimation	—Unverified	0
Who2com: Collaborative Perception via Learnable Handshake Communication	Mar 21, 2020	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	1
Explainable Object-induced Action Decision for Autonomous Vehicles	Mar 20, 2020	Autonomous DrivingAutonomous Vehicles	CodeCode Available	1
A Robotic 3D Perception System for Operating Room Environment Awareness	Mar 20, 2020	3D Semantic SegmentationScene Segmentation	—Unverified	0
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways	Mar 18, 2020	3D Semantic SegmentationAutonomous Driving	CodeCode Available	1
Neural Mesh Refiner for 6-DoF Pose Estimation	Mar 17, 2020	Autonomous DrivingInstance Segmentation	—Unverified	0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset	Mar 16, 2020	Material RecognitionObject Recognition	—Unverified	0
Scene Completeness-Aware Lidar Depth Completion for Driving Scenario	Mar 15, 2020	Depth CompletionRGBD Semantic Segmentation	CodeCode Available	1
Probabilistic Future Prediction for Video Scene Understanding	Mar 13, 2020	Future predictionOptical Flow Estimation	—Unverified	0
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery	Mar 10, 2020	Multi-Task LearningScene Understanding	CodeCode Available	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