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

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Showing 601–610 of 1723 papers

TitleStatusHype
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Anticipating Object State Changes in Long Procedural Videos—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
Boundary Seeking GANs—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
Bottom-up Instance Segmentation using Deep Higher-Order CRFs—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