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 1601–1610 of 1723 papers

TitleStatusHype
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane—0
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models—0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment—0
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding—0
FHGS: Feature-Homogenized Gaussian Splatting—0
GPT-4V Explorations: Mining Autonomous Driving—0
Show:102550
← PrevPage 161 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