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 826–850 of 1723 papers

TitleStatusHype
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
A pooling based scene text proposal technique for scene text reading in the wild—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
DSNet: An Efficient CNN for Road Scene Segmentation—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
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
Boundary Seeking GANs—0
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving—0
Bottom-up Instance Segmentation using Deep Higher-Order CRFs—0
Anticipating Object State Changes in Long Procedural Videos—0
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition—0
DORSal: Diffusion for Object-centric Representations of Scenes et al—0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding—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