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 311–320 of 1723 papers

TitleStatusHype
ChatSplat: 3D Conversational Gaussian Splatting—0
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene UnderstandingCode0
Bootstraping Clustering of Gaussians for View-consistent 3D Scene UnderstandingCode1
Quantifying the synthetic and real domain gap in aerial scene understanding—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
Reconstructing Animals and the Wild—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