Papers Road Segmentation
“Road Segmentation” 태그가 달린 논문 92편 · 필터 해제
Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation
Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perception models that distinguish walkable sidewalks from adjacent unsaf…
Semantic SegmentationDomain AdaptationRoad SegmentationIAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation
Semantic road segmentation is important for autonomous driving, but existing methods suffer severe performance degradation under low-light conditions. Many existing multi-modal fusion methods do not explicitly adapt to i…
Autonomous DrivingRoad SegmentationRoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuit…
Road SegmentationLiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
Road segmentation is a fundamental perception task for autonomous driving and intelligent robotic systems, requiring both high accuracy and real-time inference, especially for deployment on resource-constrained edge devi…
Autonomous DrivingRoad SegmentationPC-SAM: Patch-Constrained Fine-Grained Interactive Road Segmentation in High-Resolution Remote Sensing Images
Road masks obtained from remote sensing images effectively support a wide range of downstream tasks. In recent years, most studies have focused on improving the performance of fully automatic segmentation models for this…
Interactive SegmentationRoad SegmentationSingle-Eye View: Monocular Real-time Perception Package for Autonomous Driving
Amidst the rapid advancement of camera-based autonomous driving technology, effectiveness is often prioritized with limited attention to computational efficiency. To address this issue, this paper introduces LRHPerceptio…
Computational EfficiencyTrajectory PredictionAutonomous DrivingRoad SegmentationPathMamba: A Hybrid Mamba-Transformer for Topologically Coherent Road Segmentation in Satellite Imagery
Achieving both high accuracy and topological continuity in road segmentation from satellite imagery is a critical goal for applications ranging from urban planning to disaster response. State-of-the-art methods often rel…
Road SegmentationTerrain-Enhanced Resolution-aware Refinement Attention for Off-Road Segmentation
Off-road semantic segmentation suffers from thick, inconsistent boundaries, sparse supervision for rare classes, and pervasive label noise. Designs that fuse only at low resolution blur edges and propagate local errors, …
Semantic SegmentationRoad SegmentationUnsupervised Monocular Road Segmentation for Autonomous Driving via Scene Geometry
This paper presents a fully unsupervised approach for binary road segmentation (road vs. non-road), eliminating the reliance on costly manually labeled datasets. The method leverages scene geometry and temporal cues to d…
Autonomous DrivingRoad SegmentationEventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Road segmentation is pivotal for autonomous vehicles, yet achieving low latency and low compute solutions using frame based cameras remains a challenge. Event cameras offer a promising alternative. To leverage their low …
Autonomous VehiclesRoad SegmentationRoad Segmentation for ADAS/AD Applications
Accurate road segmentation is essential for autonomous driving and ADAS, enabling effective navigation in complex environments. This study examines how model architecture and dataset choice affect segmentation by trainin…
Autonomous DrivingRoad SegmentationSegmentationTechnical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Boosting Off-Road Segmentation via Photometric Distortion and Exponential Moving Average
We report on the application of a high-capacity semantic segmentation pipeline to the GOOSE 2D Semantic Segmentation Challenge for unstructured off-road environments. Using a FlashInternImage-B backbone together with a U…
2D Semantic SegmentationDecoderRoad SegmentationSegmentation+1Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles
Autonomous vehicle perception systems have traditionally relied on costly LiDAR sensors to generate precise environmental representations. In this paper, we propose a camera-only perception framework that produces Bird's…
Autonomous NavigationAutonomous VehiclesDepth EstimationMonocular Depth Estimation+5InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks to correct vehicle pose estimation in GNSS dropouts and ancho…
BenchmarkingMotion PlanningPose EstimationRoad Segmentation+1Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existi…
Autonomous DrivingAutonomous RacingClusteringComputational Efficiency+2Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery
Extraction of building footprint polygons from remotely sensed data is essential for several urban understanding tasks such as reconstruction, navigation, and mapping. Despite significant progress in the area, extracting…
DecoderExtracting Buildings In Remote Sensing ImagesGraph LearningRoad SegmentationTrajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their t…
Autonomous DrivingRoad SegmentationPathfinder for Low-altitude Aircraft with Binary Neural Network
A prior global topological map (e.g., the OpenStreetMap, OSM) can boost the performance of autonomous mapping by a ground mobile robot. However, the prior map is usually incomplete due to lacking labeling in partial path…
BinarizationPathfinderRoad SegmentationUdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised
Road segmentation is a critical task for autonomous driving systems, requiring accurate and robust methods to classify road surfaces from various environmental data. Our work introduces an innovative approach that integr…
Autonomous DrivingRoad SegmentationSegmentationBrightearth roads: Towards fully automatic road network extraction from satellite imagery
The modern road network topology comprises intricately designed structures that introduce complexity when automatically reconstructing road networks. While open resources like OpenStreetMap (OSM) offer road networks with…
Road Segmentation