Polymap: generating high definition map based on rasterized polygons
The perception of high-definition maps is an integral component of environmental perception in autonomous driving systems. Existing research have often focused on online construction of high-definition maps. For instance, the Maptr[9] series employ a detection-based method to output vectorized map instances parallelly in an end-to-end manner. However, despite their capability for real-time construction, detection-based methods are observed to lack robust generalizability[19], which hampers their applicability in auto-labeling systems. Therefore, aiming to improve the generalizability, we reinterpret road elements as rasterized polygons and design a concise framework based on instance segmentation. Initially, a segmentation-based transformer is employed to deliver instance masks in an end-to-end manner; succeeding this step, a Potrace-based[17] post-processing module is used to ultimately yield vectorized map elements. Quantitative results attained on the Nuscene[1] dataset substantiate the effectiveness and generaliz-ability of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Instance SegmentationAutonomous DrivingSimilar Papers 제목 키워드 기반
Topological Map Extraction from Overhead Images
We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper directly extracts the topological…
Semantic SegmentationBuilding outline delineation: From aerial images to polygons with an improved end-to-end learning framework
ep learning methods based upon convolutional neural networks (CNNs) have demonstrated impressive performance in the task of building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. In t…
Instance SegmentationSemantic SegmentationLearning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network
Geometric shape classification of vector polygons remains a non-trivial learning task in spatial analysis. Previous studies mainly focus on devising deep learning approaches for representation learning of rasterized vect…
Representation LearningRe:PolyWorld - A Graph Neural Network for Polygonal Scene Parsing
While most state-of-the-art instance segmentation methods produce pixel-wise segmentation masks, numerous applications demand precise vector polygons of detected objects instead of rasterized output. This paper propo…
Graph Neural NetworkInstance SegmentationScene ParsingSemantic Segmentation+1PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized o…
Graph Neural NetworkInstance SegmentationSegmentationSemantic Segmentation