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 1326–1350 of 1723 papers

TitleStatusHype
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations—0
UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration—0
Unsupervised 3D Structure Inference from Category-Specific Image Collections—0
Unsupervised Discovery and Composition of Object Light Fields—0
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
Urban Scene Diffusion through Semantic Occupancy Map—0
User Identification: A Key Enabler for Multi-User Vision-Aided Communications—0
Using Image Priors to Improve Scene Understanding—0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving—0
VideoGameBunny: Towards vision assistants for video games—0
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation—0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Vision-Language Embodiment for Monocular Depth Estimation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Visual Affordance and Function Understanding: A Survey—0
Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting—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