SOTAVerified

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

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Showing 1076–1100 of 1723 papers

TitleStatusHype
Joint stereo 3D object detection and implicit surface reconstructionCode0
Cerberus Transformer: Joint Semantic, Affordance and Attribute ParsingCode1
Panoptic Segmentation Meets Remote Sensing—0
Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot—0
Grounded Situation Recognition with TransformersCode1
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D DataCode1
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion—0
Learning Object-Centric Representations of Multi-Object Scenes from Multiple ViewsCode1
Robust deep learning-based semantic organ segmentation in hyperspectral images—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
When Neural Networks Using Different Sensors Create Similar Features—0
Panoptic 3D Scene Reconstruction From a Single RGB ImageCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
A Versatile and Efficient Reinforcement Learning Framework for Autonomous DrivingCode1
Semantic Detection of Potential Wind-borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety—0
PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB ImageCode1
Adversarial Scene Reconstruction and Object Detection System for Assisting Autonomous Vehicle—0
Monocular Depth Estimation with Sharp Boundary—0
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard ImagesCode1
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation—0
Semantic Dense Reconstruction with Consistent Scene Segments—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
Referring Self-supervised Learning on 3D Point Cloud—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3DCode1
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified