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 151–175 of 1723 papers

TitleStatusHype
PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic SegmentationCode1
Dense Audio-Visual Event Localization under Cross-Modal Consistency and Multi-Temporal Granularity CollaborationCode1
WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language ModelCode1
LLaVA-SpaceSGG: Visual Instruct Tuning for Open-vocabulary Scene Graph Generation with Enhanced Spatial RelationsCode1
Bootstraping Clustering of Gaussians for View-consistent 3D Scene UnderstandingCode1
ROOT: VLM based System for Indoor Scene Understanding and BeyondCode1
TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene UnderstandingCode1
LoLI-Street: Benchmarking Low-Light Image Enhancement and BeyondCode1
DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image FusionCode1
LED: Light Enhanced Depth Estimation at NightCode1
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
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based DecodersCode1
CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving ScenesCode1
Uni-DVPS: Unified Model for Depth-Aware Video Panoptic SegmentationCode1
A Two-Stage Masked Autoencoder Based Network for Indoor Depth CompletionCode1
MuirBench: A Comprehensive Benchmark for Robust Multi-image UnderstandingCode1
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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