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 361–370 of 1723 papers

TitleStatusHype
Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors—0
3D Vision-Language Gaussian Splatting—0
Test-Time Intensity Consistency Adaptation for Shadow Detection—0
A transition towards virtual representations of visual scenes—0
Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments—0
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy—0
Diffusion Models in 3D Vision: A Survey—0
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—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
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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