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 671–680 of 1723 papers

TitleStatusHype
Depth Estimation using Weighted-loss and Transfer Learning—0
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models—0
Real-time Approximate Bayesian Computation for Scene Understanding—0
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—0
Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics—0
Language-Assisted 3D Scene Understanding—0
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding—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