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 1051–1075 of 1723 papers

TitleStatusHype
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual ScenariosCode0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene GraphsCode0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—0
Transavs: End-To-End Audio-Visual Segmentation With Transformer—0
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation—0
Learning-based Relational Object Matching Across Views—0
ArK: Augmented Reality with Knowledge Interactive Emergent Ability—0
Neural Implicit Dense Semantic SLAM—0
Compositional 3D Human-Object Neural Animation—0
ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding—0
Factored Neural Representation for Scene Understanding—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Semantic Segmentation with High Inference Speed in Off-Road EnvironmentsCode0
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation—0
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene UnderstandingCode0
Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings—0
OVeNet: Offset Vector Network for Semantic SegmentationCode0
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation—0
Uni-Fusion: Universal Continuous Mapping—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