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 31–40 of 1723 papers

TitleStatusHype
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
Gaussian Grouping: Segment and Edit Anything in 3D ScenesCode2
GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous DrivingCode2
Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionCode2
An Egocentric Vision-Language Model based Portable Real-time Smart AssistantCode2
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingCode2
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene UnderstandingCode2
GALIP: Generative Adversarial CLIPs for Text-to-Image SynthesisCode2
Grounded 3D-LLM with Referent TokensCode2
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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