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 401–425 of 1723 papers

TitleStatusHype
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction—0
Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving—0
Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning EraCode2
EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular VideoCode3
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting—0
Leaky Wave Antenna-Equipped RF Chipless Tags for Orientation Estimation—0
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene UnderstandingCode0
UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban ScenariosCode1
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
Str-L Pose: Integrating Point and Structured Line for Relative Pose Estimation in Dual-Graph—0
RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured EnvironmentsCode2
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
Interactive Occlusion Boundary Estimation through Exploitation of Synthetic Data—0
RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language ModelsCode1
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images—0
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based DecodersCode1
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Making Large Language Models Better Planners with Reasoning-Decision Alignment—0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic SegmentationCode0
OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene UnderstandingCode1
Near, far: Patch-ordering enhances vision foundation models' scene understanding—0
3D-Aware Instance Segmentation and Tracking in Egocentric Videos—0
SceneGPT: A Language Model for 3D Scene Understanding—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