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
DAWN: Vehicle Detection in Adverse Weather Nature Dataset—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—0
OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting—0
OpenSU3D: Open World 3D Scene Understanding using Foundation Models—0
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding—0
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation—0
Open-Vocabulary Octree-Graph for 3D Scene Understanding—0
Open-Vocabulary SAM3D: Towards Training-free Open-Vocabulary 3D Scene Understanding—0
Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments—0
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Optical flow and scene flow estimation: A survey—0
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction—0
DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference—0
DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning—0
DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion—0
Using Image Priors to Improve Scene Understanding—0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos—0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Cross-modal Learning for Multi-modal Video Categorization—0
Panoptic Edge Detection—0
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation—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