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 426–450 of 1723 papers

TitleStatusHype
SceneGPT: A Language Model for 3D Scene Understanding—0
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors—0
Spherical World-Locking for Audio-Visual Localization in Egocentric Videos—0
DeepInteraction++: Multi-Modality Interaction for Autonomous DrivingCode3
Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D GaussianCode1
Complete 3d relationships extraction modality alignment network for 3d dense captioning—0
A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain GapCode0
DEF-oriCORN: efficient 3D scene understanding for robust language-directed manipulation without demonstrations—0
From Feature Importance to Natural Language Explanations Using LLMs with RAGCode0
Dynamic Scene Understanding through Object-Centric Voxelization and Neural RenderingCode1
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention UnderstandingCode0
GP-VLS: A general-purpose vision language model for surgery—0
Answerability Fields: Answerable Location Estimation via Diffusion Models—0
3D Question Answering for City Scene Understanding—0
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision—0
InLUT3D: Challenging real indoor dataset for point cloud analysis—0
VideoGameBunny: Towards vision assistants for video games—0
A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection InferenceCode0
GaussianBeV: 3D Gaussian Representation meets Perception Models for BeV Segmentation—0
MC-PanDA: Mask Confidence for Panoptic Domain AdaptationCode0
OpenSU3D: Open World 3D Scene Understanding using Foundation Models—0
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation—0
Training-Free Model Merging for Multi-target Domain Adaptation—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