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 201–225 of 1723 papers

TitleStatusHype
Label-Efficient LiDAR Panoptic Segmentation—0
vS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding—0
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond—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
Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction TuningCode2
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
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
CrossOver: 3D Scene Cross-Modal AlignmentCode3
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
NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLMCode2
Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation CorrentropyCode1
FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation—0
3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning—0
sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
Event-aided Semantic Scene CompletionCode1
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis—0
Integrating LMM Planners and 3D Skill Policies for Generalizable Manipulation—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