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 576–600 of 1723 papers

TitleStatusHype
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain TransferCode1
Stealing Stable Diffusion Prior for Robust Monocular Depth EstimationCode1
Embodied Understanding of Driving ScenariosCode3
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding—0
HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes—0
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingCode2
WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model ResearchCode1
One model to use them all: Training a segmentation model with complementary datasetsCode0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment—0
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene UnderstandingCode0
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding—0
Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene UnderstandingCode3
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive ReviewCode1
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language ModelsCode4
InCoRo: In-Context Learning for Robotics Control with Feedback Loops—0
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm PerspectivesCode2
SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAMCode3
Neural Language of Thought Models—0
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation dataCode0
Non-central panorama indoor datasetCode0
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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