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

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Showing 576–600 of 1723 papers

TitleStatusHype
Improving Online Lane Graph Extraction by Object-Lane Clustering—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model—0
BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning—0
Dynamic Scene Understanding from Vision-Language Representations—0
Building an Affordances Map with Interactive Perception—0
A pooling based scene text proposal technique for scene text reading in the wild—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems—0
Image-to-Height Domain Translation for Synthetic Aperture Sonar—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection—0
Image-Graph-Image Translation via Auto-Encoding—0
Image Parsing with Stochastic Scene Grammar—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—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