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 951–960 of 1723 papers

TitleStatusHype
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy—0
Evaluation of Multimodal Semantic Segmentation using RGB-D Data—0
Event fields: Capturing light fields at high speed, resolution, and dynamic range—0
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond—0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
EvSegSNN: Neuromorphic Semantic Segmentation for Event Data—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