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

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Showing 1451–1460 of 1723 papers

TitleStatusHype
Robust Visual Localization via Semantic-Guided Multi-Scale Transformer—0
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms—0
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness—0
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
S4C: Self-Supervised Semantic Scene Completion with Neural Fields—0
Safety Assessment for Autonomous Systems' Perception Capabilities—0
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data—0
SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes—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