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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1581–1590 of 1723 papers

TitleStatusHype
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—0
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions—0
J-MOD^2: Joint Monocular Obstacle Detection and Depth Estimation—0
Direction-Aware Semi-Dense SLAM—0
Matterport3D: Learning from RGB-D Data in Indoor EnvironmentsCode0
Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision—0
Reasoning with shapes: profiting cognitive susceptibilities to infer linear mapping transformations between shapes—0
Semantic Foggy Scene Understanding with Synthetic Data—0
3D Pose Regression using Convolutional Neural Networks—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