Paper Info
Title
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Abstract
Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, and has been point-wisely annotated with both hierarchical and instance-based labels. Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies. To solve this problem, a two-stage method including multi-task (MT) learning and hierarchical ensemble (HE) with consistency consideration is proposed. Experimental results demonstrate the superiority of the proposed method and potential advantages of our hierarchical annotations. In addition, we benchmark results of semantic and instance segmentation, which is accessible online at https://3d.dataset.site with the dataset and all source codes.
Year
DOI
Venue
2020
10.1145/3394171.3413661
MM '20: The 28th ACM International Conference on Multimedia Seattle WA USA October, 2020
DocType
ISSN
ISBN
Conference
Proceedings of the 28th ACM International Conference on Multimedia 2020
978-1-4503-7988-5
Citations 
PageRank 
References 
1
0.35
26
Authors
8
Name
Order
Citations
PageRank
Xinke Li112.71
Chongshou Li2154.94
Zekun Tong312.37
Andrew Lim493789.78
Junsong Yuan53703187.68
Yuwei Wu625629.65
Jing Tang7614.38
Raymond Huang810.35