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 1326–1350 of 1723 papers

TitleStatusHype
Self-Supervised Scene De-occlusionCode1
MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation—0
Context Prior for Scene SegmentationCode1
PointGroup: Dual-Set Point Grouping for 3D Instance SegmentationCode1
Occlusion-Aware Depth Estimation with Adaptive Normal ConstraintsCode1
Semantic Segmentation of Underwater Imagery: Dataset and BenchmarkCode1
Learning Human-Object Interaction Detection using Interaction PointsCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
LayoutMP3D: Layout Annotation of Matterport3DCode1
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point CloudsCode1
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings—0
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
One-Shot GAN Generated Fake Face Detection—0
High-Accuracy Facial Depth Models derived from 3D Synthetic Data—0
SaccadeNet: A Fast and Accurate Object DetectorCode1
Adversarial Attacks on Monocular Depth Estimation—0
Who2com: Collaborative Perception via Learnable Handshake CommunicationCode1
Explainable Object-induced Action Decision for Autonomous VehiclesCode1
A Robotic 3D Perception System for Operating Room Environment Awareness—0
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban RoadwaysCode1
Neural Mesh Refiner for 6-DoF Pose Estimation—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Scene Completeness-Aware Lidar Depth Completion for Driving ScenarioCode1
Probabilistic Future Prediction for Video Scene Understanding—0
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted SurgeryCode0
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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