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 326–350 of 1723 papers

TitleStatusHype
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion ModelsCode2
RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics—0
ROOT: VLM based System for Indoor Scene Understanding and BeyondCode1
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations—0
Multimodal 3D Reasoning Segmentation with Complex Scenes—0
GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous DrivingCode2
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation—0
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse WeatherCode0
Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications—0
MGNiceNet: Unified Monocular Geometric Scene UnderstandingCode0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth EstimationCode0
Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm—0
TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene UnderstandingCode1
Content-Aware Preserving Image Generation—0
OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with Fused Geometric and Semantic GuidanceCode2
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Graph-Based Multi-Modal Sensor Fusion for Autonomous Driving—0
Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting—0
Multi-task Geometric Estimation of Depth and Surface Normal from Monocular 360° ImagesCode0
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDARCode2
Symbolic Graph Inference for Compound Scene Understanding—0
UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration—0
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous DrivingCode4
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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