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 501–550 of 1723 papers

TitleStatusHype
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imageryCode1
Grounded 3D-LLM with Referent TokensCode2
A Preprocessing and Postprocessing Voxel-based Method for LiDAR Semantic Segmentation Improvement in Long Distance—0
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language ModelsCode7
4D Panoptic Scene Graph GenerationCode3
3D Shape Augmentation with Content-Aware Shape Resizing—0
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation—0
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for ControlCode1
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionCode1
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous DrivingCode3
OpenESS: Event-based Semantic Scene Understanding with Open VocabulariesCode2
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAM—0
Seeing Beyond Classes: Zero-Shot Grounded Situation Recognition via Language Explainer—0
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
BACS: Background Aware Continual Semantic SegmentationCode0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth EstimationCode2
AccidentBlip: Agent of Accident Warning based on MA-former—0
Multimodal 3D Object Detection on Unseen Domains—0
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape ReconstructionCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Depth Estimation using Weighted-loss and Transfer Learning—0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles—0
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation—0
DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning—0
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding—0
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation—0
Sigma: Siamese Mamba Network for Multi-Modal Semantic SegmentationCode3
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects—0
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic FieldsCode1
Improving Visual Recognition with Hyperbolical Visual Hierarchy MappingCode1
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance FieldsCode2
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements—0
360+x: A Panoptic Multi-modal Scene Understanding Dataset—0
Adapting to Length Shift: FlexiLength Network for Trajectory Prediction—0
Neural Radiance Field-based Visual Rendering: A Comprehensive Review—0
VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object DetectionCode1
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes—0
Efficient 3D Instance Mapping and Localization with Neural Fields—0
Object Pose Estimation via the Aggregation of Diffusion FeaturesCode1
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations—0
Calib3D: Calibrating Model Preferences for Reliable 3D Scene UnderstandingCode2
Is Your LiDAR Placement Optimized for 3D Scene Understanding?Code2
DOCTR: Disentangled Object-Centric Transformer for Point Scene UnderstandingCode0
Show:102550
← PrevPage 11 of 35Next →
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