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 551–575 of 1723 papers

TitleStatusHype
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction—0
AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D ScansCode1
Multi-Task Learning with Multi-Task Optimization—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Exosense: A Vision-Based Scene Understanding System For Exoskeletons—0
3D Object Detection from Point Cloud via Voting Step DiffusionCode0
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field—0
Volumetric Environment Representation for Vision-Language NavigationCode2
What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal ModelsCode1
Geometric Constraints in Deep Learning Frameworks: A Survey—0
Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain AdaptationCode0
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting—0
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving—0
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding—0
OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy RepresentationCode0
Urban Scene Diffusion through Semantic Occupancy Map—0
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Agent3D-Zero: An Agent for Zero-shot 3D Understanding—0
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance FieldsCode0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields—0
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning—0
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
MoAI: Mixture of All Intelligence for Large Language and Vision ModelsCode3
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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