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 911–920 of 1723 papers

TitleStatusHype
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—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