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 601–625 of 1723 papers

TitleStatusHype
Non-central panorama indoor datasetCode0
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems—0
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature RepresentationCode1
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
Pixel-Wise Recognition for Holistic Surgical Scene UnderstandingCode2
ICGNet: A Unified Approach for Instance-Centric GraspingCode0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection—0
GARField: Group Anything with Radiance FieldsCode3
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding—0
RSUD20K: A Dataset for Road Scene Understanding In Autonomous DrivingCode1
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
VLP: Vision Language Planning for Autonomous Driving—0
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the WildCode0
3DMIT: 3D Multi-modal Instruction Tuning for Scene UnderstandingCode1
FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding—0
Unsupervised 3D Structure Inference from Category-Specific Image Collections—0
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness—0
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach—0
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular VideoCode0
Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and ConsistencyCode0
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—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