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 1141–1150 of 1723 papers

TitleStatusHype
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP—0
Polarimetric Spatio-Temporal Light Transport Probing—0
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmentation—0
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation—0
PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation—0
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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