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 1001–1025 of 1723 papers

TitleStatusHype
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Object Recognition System on a Tactile Device for Visually Impaired—0
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network—0
ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects—0
Occam's LGS: A Simple Approach for Language Gaussian Splatting—0
OccLE: Label-Efficient 3D Semantic Occupancy Prediction—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—0
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding—0
Occlusion Boundary Detection via Deep Exploration of Context—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
Deep Semantic Segmentation of Natural and Medical Images: A Review—0
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding—0
OmniIndoor3D: Comprehensive Indoor 3D Reconstruction—0
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
Deep Scene Text Detection with Connected Component Proposals—0
On Efficient Real-Time Semantic Segmentation: A Survey—0
Urban Scene Diffusion through Semantic Occupancy Map—0
One-Shot GAN Generated Fake Face Detection—0
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding—0
Deep Point Cloud Simplification for High-quality Surface Reconstruction—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Deeply Learned Attributes for Crowded 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