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 881–890 of 1723 papers

TitleStatusHype
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements—0
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency—0
MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
Uni-Fusion: Universal Continuous Mapping—0
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations—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