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 521–530 of 1723 papers

TitleStatusHype
AccidentBlip: Agent of Accident Warning based on MA-former—0
Multimodal 3D Object Detection on Unseen Domains—0
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape ReconstructionCode1
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Depth Estimation using Weighted-loss and Transfer Learning—0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation—0
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles—0
Show:102550
← PrevPage 53 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