Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds

by   Ruisheng Wang, et al.

Urban modeling from LiDAR point clouds is an important topic in computer vision, computer graphics, photogrammetry and remote sensing. 3D city models have found a wide range of applications in smart cities, autonomous navigation, urban planning and mapping etc. However, existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. Lack of building datasets has become a major obstacle for applying deep learning technology to specific domains such as urban modeling. In this paper, we present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art algorithms including handcrafted and deep feature based methods. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises. The Building3D is the first and largest urban-scale building modeling benchmark, allowing a comparison of supervised and self-supervised learning methods. We believe that our Building3D will facilitate future research on urban modeling, aerial path planning, mesh simplification, and semantic/part segmentation etc.


page 1

page 6

page 8


Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

An essential prerequisite for unleashing the potential of supervised dee...

UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation

We present the UrbanBIS benchmark for large-scale 3D urban understanding...

Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao

UAVs have become one of the widely used remote sensing platforms and pla...

Digital twins for city simulation: Automatic, efficient, and robust mesh generation for large-scale city modeling and simulation

The concept of creating digital twins, connected digital models of physi...

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusio...

HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point clouds

Many existing 3D semantic segmentation methods, deep learning in compute...

Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset

We introduce a new outdoor urban 3D pointcloud dataset, covering a total...

Please sign up or login with your details

Forgot password? Click here to reset