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 976–1000 of 1723 papers

TitleStatusHype
Factor Graph based 3D Multi-Object Tracking in Point Clouds—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding—0
FastLGS: Speeding up Language Embedded Gaussians with Feature Grid Mapping—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Fast Object Detection with a Machine Learning Edge Device—0
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding—0
FHGS: Feature-Homogenized Gaussian Splatting—0
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation—0
Floorplan-SLAM: A Real-Time, High-Accuracy, and Long-Term Multi-Session Point-Plane SLAM for Efficient Floorplan Reconstruction—0
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition—0
FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding—0
FMLGS: Fast Multilevel Language Embedded Gaussians for Part-level Interactive Agents—0
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization—0
Framework for 2D Ad placements in LinearTV—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance—0
FroDO: From Detections to 3D Objects—0
FroDO: From Detections to 3D Objects—0
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation—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