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 891–900 of 1723 papers

TitleStatusHype
Neural Radiance Field-based Visual Rendering: A Comprehensive Review—0
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes—0
Efficient 3D Instance Mapping and Localization with Neural Fields—0
DOCTR: Disentangled Object-Centric Transformer for Point Scene UnderstandingCode0
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations—0
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction—0
Multi-Task Learning with Multi-Task Optimization—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
3D Object Detection from Point Cloud via Voting Step DiffusionCode0
Show:102550
← PrevPage 90 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