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 901–925 of 1723 papers

TitleStatusHype
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field—0
Exosense: A Vision-Based Scene Understanding System For Exoskeletons—0
Geometric Constraints in Deep Learning Frameworks: A Survey—0
Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain AdaptationCode0
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving—0
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting—0
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Agent3D-Zero: An Agent for Zero-shot 3D Understanding—0
OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy RepresentationCode0
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding—0
Urban Scene Diffusion through Semantic Occupancy Map—0
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance FieldsCode0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields—0
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
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
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
One model to use them all: Training a segmentation model with complementary datasetsCode0
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
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—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