Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

By Xinke Li, Chongshou Li, Zekun Tong, A. Lim, Junsong Yuan, Yuwei Wu, Jing Tang, Raymond Huang. 2020

73 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Environmental Engineering, Geology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. B. Boom
2. Weixiao Gao
3. Liangliang Nan
4. Hugo Ledoux

In Environmental Engineering

1. B. Boom
2. Weixiao Gao
3. Liangliang Nan
4. Hugo Ledoux

In Geology

1. B. Boom
2. Weixiao Gao
3. Liangliang Nan
4. Hugo Ledoux

Citations in the corpus

• Class Gradient Projection For Continual Learning (2022, background): “Although the modern deep learning network (DNN) has shown good performance on the task at hand [5, 12, 24, 41, 46, 47], they always violently forget the previous knowledge when they learn new tasks.”
• SUM: A Benchmark Dataset of Semantic Urban Meshes (2021, background): “The Campus3D [29] is to our knowledge the first aerial point cloud benchmark.”