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 301–325 of 1723 papers

TitleStatusHype
Stag-1: Towards Realistic 4D Driving Simulation with Video Generation ModelCode2
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene UnderstandingCode2
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection—0
SparseLGS: Sparse View Language Embedded Gaussian Splatting—0
BYE: Build Your Encoder with One Sequence of Exploration Data for Long-Term Dynamic Scene Understanding—0
LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual PreferencesCode2
A Semantic Communication System for Real-time 3D Reconstruction Tasks—0
Holistic Understanding of 3D Scenes as Universal Scene Description—0
Occam's LGS: A Simple Approach for Language Gaussian Splatting—0
ChatSplat: 3D Conversational Gaussian Splatting—0
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene UnderstandingCode0
Bootstraping Clustering of Gaussians for View-consistent 3D Scene UnderstandingCode1
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Quantifying the synthetic and real domain gap in aerial scene understanding—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial TasksCode2
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
Reconstructing Animals and the Wild—0
LoCATe-GAT: Modeling Multi-Scale Local Context and Action Relationships for Zero-Shot Action RecognitionCode0
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agentsCode0
HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving—0
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learningCode0
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
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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