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 761–770 of 1723 papers

TitleStatusHype
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy—0
Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments—0
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—0
Diffusion Models in 3D Vision: A Survey—0
In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding—0
Fast Object Detection with a Machine Learning Edge Device—0
SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models—0
RESSCAL3D++: Joint Acquisition and Semantic Segmentation of 3D Point CloudsCode0
A Critical Assessment of Visual Sound Source Localization Models Including Negative AudioCode0
Class-Agnostic Visio-Temporal Scene Sketch Semantic 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