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 626–650 of 1723 papers

TitleStatusHype
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth AmbiguityCode0
Segment Anything, Even Occluded—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
SplatTalk: 3D VQA with Gaussian Splatting—0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Improving 6D Object Pose Estimation of metallic Household and Industry Objects—0
SurgiSAM2: Fine-tuning a foundational model for surgical video anatomy segmentation and detection—0
Label-Efficient LiDAR Panoptic Segmentation—0
SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images—0
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond—0
vS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding—0
OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding—0
Floorplan-SLAM: A Real-Time, High-Accuracy, and Long-Term Multi-Session Point-Plane SLAM for Efficient Floorplan Reconstruction—0
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion—0
AAD-LLM: Neural Attention-Driven Auditory Scene Understanding—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
Hierarchical Context Transformer for Multi-level Semantic Scene UnderstandingCode0
AVD2: Accident Video Diffusion for Accident Video Description—0
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning—0
Understanding and Evaluating Hallucinations in 3D Visual Language Models—0
Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review—0
3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning—0
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation—0
sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views—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