# Mesh-based 3D Textured Urban Mapping

arXiv: https://arxiv.org/abs/1708.05543 · HTML: https://mlsanity.com/paper/1708.05543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 5 of 5)

- [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021)
- [Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations](https://mlsanity.com/paper/2111.13152/) (2021)
- [Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction](https://mlsanity.com/paper/2406.16289/) (2024)
- [Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles](https://mlsanity.com/paper/2101.06393/) (2021)
- [Online Localisation and Colored Mesh Reconstruction Architecture for 3D Visual Feedback in Robotic Exploration Missions](https://mlsanity.com/paper/2207.10489/) (2022)

## Abstract (arXiv)

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.