AI Computer Vision & Graphics Research

Introducing Holicity: A City-Scale Dataset for Learning Holistic 3D Structures

HoliCity, a city-scale dataset and all-in-one data platform for research into learning abstracted high-level holistic 3D structures derived from city CAD (computer-aided design) models.

Researchers from UC Berkeley, Stanford and Bytedance recently introduced HoliCity, a city-scale dataset and all-in-one data platform for research into learning abstracted high-level holistic 3D structures derived from city CAD (computer-aided design) models. A rigorous annotation pipeline and associated tools enable continuous increases in the scale and richness of the dataset, supporting the study and evaluation of a wide spectrum of 3D vision methods, from low-level to high-level.

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3D vision technologies have been in increasing demand in recent years. Although the development of robust point features such as SIFT, ORB, SfM and SLAM has greatly improved 3D vision performance; their resulting point clouds are still noisy, incomplete, and difficult to use directly in many real-world applications.

Inspired by the tremendous success of deep convolutional neural networks (CNNs) in image classification, 3D vision researchers have proposed a variety of neural network-based approaches to extract high-level holistic structures from images, such as wireframes, planes, cuboids, vanishing points, room layouts, and building layouts.

HoliCity includes 6,300 high-resolution real-world panoramas accurately aligned with a 3D CAD model of downtown London with more than 20 km^2 of area, and was designed to address the high cost of data preparation. Rather than relying on expensive vehicle-mounted LiDAR scanners, HoliCity leverages existing high-quality 3D CAD city models and street-view imagery from geographic information system (GIS) collection.

The researchers conducted multiple experiments with their CAD model-based data platform for 3D vision research, testing potential applications and the platform’s generalizability from and to other datasets. The results confirm HoliCity’s usefulness as an urban environment data platform for a range of 3D vision research areas.

Currently, HoliCity focuses on modelling building architectures. In the future, the researchers plan to extend the datasets to allow the modelling of moving objects such as pedestrians and automobiles in urban environments.

The paper HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures is on arXiv.


Analyst: Grace Duan | Editor: Michael Sarazen; Fangyu Cai



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