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 1001–1025 of 1723 papers

TitleStatusHype
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction—0
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding—0
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery—0
Fusion Based Holistic Road Scene Understanding—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting—0
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning—0
GameVLM: A Decision-making Framework for Robotic Task Planning Based on Visual Language Models and Zero-sum Games—0
GANspection—0
GaussianBeV: 3D Gaussian Representation meets Perception Models for BeV Segmentation—0
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting—0
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data—0
General-Purpose Aerial Intelligent Agents Empowered by Large Language Models—0
Generating Robot Constitutions & Benchmarks for Semantic Safety—0
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis—0
Generative Video Transformer: Can Objects be the Words?—0
Geometric Constrained Non-Line-of-Sight Imaging—0
Geometric Constraints in Deep Learning Frameworks: A Survey—0
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization—0
Glass Segmentation Using Intensity and Spectral Polarization Cues—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane—0
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models—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