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

arXiv: https://arxiv.org/abs/2008.04968 · HTML: https://mlsanity.com/paper/2008.04968/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 27 of 27)

- [OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation](https://mlsanity.com/paper/2309.00616/) (2023)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://mlsanity.com/paper/2201.04494/) (2022)
- [Pyramid Diffusion for Fine 3D Large Scene Generation](https://mlsanity.com/paper/2311.12085/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation](https://mlsanity.com/paper/2305.02627/) (2023)
- [SUM: A Benchmark Dataset of Semantic Urban Meshes](https://mlsanity.com/paper/2103.00355/) (2021)
- [CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data](https://mlsanity.com/paper/2310.18773/) (2023)
- [A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation](https://mlsanity.com/paper/2405.11903/) (2024)
- [Class Gradient Projection For Continual Learning](https://mlsanity.com/paper/2311.14905/) (2023)
- [ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation](https://mlsanity.com/paper/2404.10699/) (2024)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Controllable 3D Outdoor Scene Generation via Scene Graphs](https://mlsanity.com/paper/2503.07152/) (2025)
- [OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World](https://mlsanity.com/paper/2101.09641/) (2021)
- [Efficient Urban-scale Point Clouds Segmentation with BEV Projection](https://mlsanity.com/paper/2109.09074/) (2021)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives](https://mlsanity.com/paper/2205.12627/) (2022)
- [3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model](https://mlsanity.com/paper/2501.17534/) (2025)
- [HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point clouds](https://mlsanity.com/paper/2307.07976/) (2023)
- [CitySeg: A 3D Open Vocabulary Semantic Segmentation Foundation Model in City-scale Scenarios](https://mlsanity.com/paper/2508.09470/) (2025)
- [CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks](https://mlsanity.com/paper/2404.03191/) (2024)
- [OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities](https://mlsanity.com/paper/2404.07711/) (2024)
- [SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes](https://mlsanity.com/paper/2503.15300/) (2025)
- [Late-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision](https://mlsanity.com/paper/2511.16650/) (2025)
- [A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2605.17131/) (2026)
- [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024)

## Abstract (arXiv)

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.