Papers road scene understanding
“road scene understanding” 태그가 달린 논문 20편 · 필터 해제
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems
The integration of Large Language Models (LLMs) with computer vision is profoundly transforming perception tasks like image segmentation. For intelligent transportation systems (ITS), where accurate scene understanding i…
Autonomous DrivingImage Segmentationroad scene understandingScene Understanding+1Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding
Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction. However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability an…
Autonomous DrivingRAGRetrieval-augmented Generationroad scene understanding+2Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the context of intelligent vehicles, leveraging …
Continual LearningMulti-Task Learningroad scene understandingScene UnderstandingRSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment. However, recent object detectors tailored for learning on datasets collected from certain geographical loca…
Autonomous DrivingBenchmarkingObjectroad scene understanding+1Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather
Road scene understanding tasks have recently become crucial for self-driving vehicles. In particular, real-time semantic segmentation is indispensable for intelligent self-driving agents to recognize roadside objects in …
Autonomous DrivingGPUReal-Time Semantic Segmentationroad scene understanding+2BlindSpotNet: Seeing Where We Cannot See
We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual …
Depth EstimationMonocular Depth Estimationroad scene understandingScene Understanding+1SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks
Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable manner, presents an opportun…
Autonomous DrivingData AugmentationMultiple Object Trackingobject-detection+3Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as a binary classification one, e.g. associ…
Binary ClassificationContrastive LearningOpen-Ended Question Answeringroad scene understanding+2PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the proje…
Image Segmentationroad scene understandingRoad SegmentationScene Understanding+2MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of ro…
Autonomous VehiclesClassificationDepth EstimationDepth Prediction+8DSNet: An Efficient CNN for Road Scene Segmentation
Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing r…
Autonomous DrivingGPUroad scene understandingScene Segmentation+1Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an eme…
Autonomous Drivingroad scene understandingScene UnderstandingSemantic SegmentationAffordance Learning In Direct Perception for Autonomous Driving
Recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles are using mediated perception approach for path planning and control, wh…
AttributeAutonomous DrivingAutonomous Vehiclesroad scene understanding+1IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
While several datasets for autonomous navigation have become available in recent years, they tend to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a s…
Autonomous NavigationDomain AdaptationFew-Shot Learningroad scene understanding+2Auxiliary Tasks in Multi-task Learning
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the net…
Depth EstimationMulti-Task Learningroad scene understandingScene Understanding+1Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
Scene understanding for autonomous vehicles is a challenging computer vision task, with recent advances in convolutional neural networks (CNNs) achieving results that notably surpass prior traditional feature driven appr…
Autonomous Vehiclesroad scene understandingScene UnderstandingSemantic Segmentation+2The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annot…
DiversityImage SegmentationInstance Segmentationroad scene understanding+3Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding
We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of v…
Autonomous Drivingroad scene understandingScene UnderstandingA Continuous Occlusion Model for Road Scene Understanding
We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling o…
modelMotion Segmentationobject-detectionObject Detection+3Fusion Based Holistic Road Scene Understanding
This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, we propose an approach that jointly tackles object-level image segmentat…
ClusteringImage SegmentationObjectroad scene understanding+3