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 1461–1470 of 1723 papers

Title	Date	Tasks	Status
Deep Optics for Monocular Depth Estimation and 3D Object Detection	Apr 18, 2019	3D Object DetectionDepth Estimation	—Unverified
DSNet: An Efficient CNN for Road Scene Segmentation	Apr 10, 2019	Autonomous DrivingGPU	—Unverified
Deep Surface Normal Estimation with Hierarchical RGB-D Fusion	Apr 6, 2019	Scene UnderstandingSurface Normal Estimation	CodeCode Available
Structured agents for physical construction	Apr 5, 2019	Deep Reinforcement LearningReinforcement Learning	—Unverified
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer	Apr 3, 2019	Deep Reinforcement LearningReinforcement Learning	CodeCode Available
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences	Apr 2, 2019	3D Semantic SegmentationScene Understanding	CodeCode Available
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields	Apr 1, 2019	3D Instance Segmentation3D Semantic Instance Segmentation	CodeCode Available
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data	Apr 1, 2019	Scene ParsingScene Understanding	CodeCode Available
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation	Mar 31, 2019	Autonomous Drivingroad scene understanding	CodeCode Available
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?	Mar 31, 2019	Human-Object Interaction DetectionObject	—Unverified

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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