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 851–900 of 1723 papers

TitleStatusHype
Dynamic Scene Understanding from Vision-Language Representations—0
MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents—0
Making Large Language Models Better Planners with Reasoning-Decision Alignment—0
Manhattan Scene Understanding via XSlit Imaging—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Adapting to Length Shift: FlexiLength Network for Trajectory Prediction—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—0
Active Scene Understanding via Online Semantic Reconstruction—0
A Continuous Occlusion Model for Road Scene Understanding—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection—0
Content Adaptive Front End For Audio Classification—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation—0
Unified Representation Space for 3D Visual Grounding—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements—0
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency—0
MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
Uni-Fusion: Universal Continuous Mapping—0
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations—0
Modeling human intuitions about liquid flow with particle-based simulation—0
Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting—0
DORSal: Diffusion for Object-centric Representations of Scenes et al—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
Monocular BEV Perception of Road Scenes via Front-to-Top View Projection—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
Monocular Depth Estimation with Sharp Boundary—0
Does CLIP perceive art the same way we do?—0
MonoGRNet: A General Framework for Monocular 3D Object Detection—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