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 1176–1200 of 1723 papers

TitleStatusHype
Self-Supervised Object Detection from Egocentric Videos—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints—0
Semantic Augmented Reality Environment with Material-Aware Physical Interactions—0
Semantic-aware Transmission for Robust Point Cloud Classification—0
Semantic Dense Reconstruction with Consistent Scene Segments—0
Semantic Detection of Potential Wind-borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Semantic Foggy Scene Understanding with Synthetic Data—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Semantic segmentation of surgical hyperspectral images under geometric domain shifts—0
SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields—0
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification—0
Semi-Supervised Learning of Multi-Object 3D Scene Representations—0
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—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