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 26–50 of 1723 papers

TitleStatusHype
AudioBench: A Universal Benchmark for Audio Large Language ModelsCode3
MoAI: Mixture of All Intelligence for Large Language and Vision ModelsCode3
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal ModelsCode3
STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor ScenesCode3
Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction TuningCode2
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene UnderstandingCode2
Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian SplattingCode2
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene UnderstandingCode2
InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene UnderstandingCode2
Grounded 3D-LLM with Referent TokensCode2
GroupViT: Semantic Segmentation Emerges from Text SupervisionCode2
GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous DrivingCode2
BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationCode2
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
HAKE: A Knowledge Engine Foundation for Human Activity UnderstandingCode2
IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D ScenesCode2
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingCode2
An Egocentric Vision-Language Model based Portable Real-time Smart AssistantCode2
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial ReasoningCode2
Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionCode2
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene UnderstandingCode2
ARKit LabelMaker: A New Scale for Indoor 3D Scene UnderstandingCode2
GALIP: Generative Adversarial CLIPs for Text-to-Image SynthesisCode2
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene UnderstandingCode2
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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