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 161–170 of 1723 papers

TitleStatusHype
Online 3D reconstruction and dense tracking in endoscopic videosCode1
UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban ScenariosCode1
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based DecodersCode1
RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language ModelsCode1
OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene UnderstandingCode1
Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D GaussianCode1
Dynamic Scene Understanding through Object-Centric Voxelization and Neural RenderingCode1
General Geometry-aware Weakly Supervised 3D Object DetectionCode1
Dual-Hybrid Attention Network for Specular Highlight RemovalCode1
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen RepresentationsCode1
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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