Polymap: generating high definition map based on rasterized polygons

TL;DR

Polymap introduces a segmentation-based transformer framework that converts rasterized polygons into high-definition maps, enhancing generalizability and accuracy for autonomous driving applications.

Contribution

The paper presents a novel approach using instance segmentation and Potrace post-processing to generate high-definition maps from rasterized polygons, improving over detection-based methods.

Findings

Effective on the Nuscene dataset

Demonstrates improved generalizability

Produces accurate vectorized map elements

Abstract

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.