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 611–620 of 1723 papers

TitleStatusHype
RSUD20K: A Dataset for Road Scene Understanding In Autonomous DrivingCode1
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
VLP: Vision Language Planning for Autonomous Driving—0
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the WildCode0
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding—0
Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and ConsistencyCode0
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness—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