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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 1723 papers

TitleStatusHype
SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data PretrainingCode2
The Coralscapes Dataset: Semantic Scene Understanding in Coral ReefsCode1
OpenLex3D: A New Evaluation Benchmark for Open-Vocabulary 3D Scene Representations—0
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving—0
MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation—0
PolarFree: Polarization-based Reflection-free ImagingCode2
PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding—0
SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language PretrainingCode3
PanopticSplatting: End-to-End Panoptic Gaussian Splatting—0
Geometric Constrained Non-Line-of-Sight Imaging—0
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling—0
ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail—0
Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene UnderstandingCode1
IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D ScenesCode2
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models—0
PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point CloudsCode0
ChatBEV: A Visual Language Model that Understands BEV Maps—0
Crab: A Unified Audio-Visual Scene Understanding Model with Explicit CooperationCode2
HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding—0
NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language ModelsCode1
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene UnderstandingCode1
Road Rage Reasoning with Vision-language Models (VLMs): Task Definition and Evaluation Dataset—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