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 701–710 of 1723 papers

TitleStatusHype
ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects—0
Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset—0
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting—0
An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds—0
SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians—0
SLGaussian: Fast Language Gaussian Splatting in Sparse Views—0
TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking—0
MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents—0
Event fields: Capturing light fields at high speed, resolution, and dynamic range—0
Visual Lexicon: Rich Image Features in Language Space—0
Show:102550
← PrevPage 71 of 173Next →
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