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 881–890 of 1723 papers

TitleStatusHype
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels—0
Depth Estimation using Weighted-loss and Transfer Learning—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Design and Evaluation of Deep Learning-Based Dual-Spectrum Image Fusion Methods—0
Designing Deep Networks for Surface Normal Estimation—0
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction—0
Diagnostics in Semantic Segmentation—0
Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis—0
DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes—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