The 3D world around us is composed of a rich variety of objects: buildings, bridges, trees, cars, rivers, and so forth, each with distinct appearance, morphology, and function. Giving machines the ability to precisely segment and label these diverse objects is of key importance to allow them to interact competently within our physical world, for applications such as scene-level robot navigation, autonomous driving, and even large-scale urban 3D modeling, which is critical for the future of smart city planning and management.
Over the past years, remarkable advances in techniques for 3D point cloud understanding have greatly boosted performance. Although these approaches achieve impressive results for object recognition and semantic segmentation, almost all of them are limited to extremely small 3D point clouds, and are difficult to be directly extended to large-scale point clouds. [Bilibili Live] [YouTube Live]
The 3rd Challenge on Large Scale Point-cloud Analysis for Urban Scenes Understanding (Urban3D) at ICCV 2023 aims to establish new benchmarks for 3D semantic and instance segmentation on urban-scale point clouds. In particular, we prime the challenge with both SensatUrban and STPLS3D datasets. SensatUrban consists of large-scale subsections of multiple urban areas in the UK. With the high quality of per-point annotations and the diverse distribution of semantic categories. STPLS3D is composed of both real-world and synthetic environments which cover more than 17 km2 of the city landscape in the U.S. with up to 18 fine-grained semantic classes and 14 instance classes. These two datasets are complementary to each other and allow us to explore a number of key research problems and directions for 3D semantic and instance learning in this workshop. We aspire to highlight the challenges faced in 3D segmentation on extremely large and dense point clouds of urban environments, sparking innovation in applications such as smart cities, digital twins, autonomous vehicles, automated asset management of large national infrastructures, and intelligent construction sites. We hope that our datasets, and this workshop could inspire the community to explore the next level of 3D learning. Specifically, We encourage researchers from a wide range of background to participate in our challenge, the topics including but not limited to:
- Semantic segmentation of large-scale 3D point clouds.
- Instance segmentation of 3D point clouds.
- Weakly (self) supervised learning in 3D point clouds analysis.
- Domain adaptation of heterogeneous 3D point clouds.
- Learning from imbalanced 3D point clouds.
- 3D point cloud acquisition & visualization.
- 3D object detection & reconstruction.
Call for Contributions
Urban3D Challenges@ICCV'2023
The Urban3D Challenges are hosted on Codalab, and can be found at:
-
Track 1: 3D Semantic Segmentation of Urban-scale Point Clouds.
- Urban3D Challenge: https://codalab.lisn.upsaclay.fr/competitions/7113
- SensatUrban dataset: http://point-cloud-analysis.cs.ox.ac.uk/
- SensatUrban API: https://github.com/QingyongHu/SensatUrban
-
Track 2: 3D Instance Segmentation of Urban-scale Point Clouds.
- STPLS3D Challenge: https://codalab.lisn.upsaclay.fr/competitions/4646
- STPLS3D dataset: https://www.stpls3d.com
- STPLS3D API: https://github.com/meidachen/STPLS3D
We are thankful to our sponsor for providing the following prizes. The prize award will be granted to the Top 3 individuals and teams for Each Challenge Track on the leaderboard that provide a valid submission.
|
$1,500 USD | courtesy of  |
|
$1,000 USD | courtesy of |
|
$500 USD | courtesy of |
Invited Keynote Speakers
University College London
Michael Batty is Bartlett Professor of Planning at University College London where he is Chair of the Centre for Advanced Spatial Analysis (CASA). He has worked on computer models of cities and their visualisation since the 1970s and has published several books, such as Cities and Complexity (MIT Press, 2005) which won the Alonso Prize of the Regional Science Association in 2011, and most recently The New Science of Cities (MIT Press, 2013). He is a Fellow of the British Academy (FBA), the Academy of Social Sciences (FAcSS) and the Royal Society (FRS), was awarded the CBE in the Queen’s Birthday Honours in 2004 and the 2013 recipient of the Laur´eat Prix International de G´eographie Vautrin Lud (generally known as the ’Nobel de G´eographie’). This year 2015 he received the Founders Medal of the Royal Geographical Society for his work on the science of cities. In 2016 he received the Gold Medal of the Royal Town Planning Institute, and the Senior Scholars Award of the Complex Systems Society.
University of Cambridge
Ioannis Brilakis is a Laing O’Rourke Professor of Construction Engineering and the Director of the Construction Information Technology Laboratory at the Division of Civil Engineering of the Department of Engineering at the University of Cambridge. He completed his PhD in Civil Engineering at the University of Illinois, Urbana Champaign in 2005. He then worked as an Assistant Professor at the Departments of Civil and Environmental Engineering, University of Michigan, Ann Arbor (2005-2008) and Georgia Institute of Technology, Atlanta (2008-2012) before moving to Cambridge in 2012 as a Laing O’Rourke Lecturer.
Xiao Xiang Zhu is the Chair Professor of Data Science with Earth Observation, Technical University of Munich, and was the Founding Head of the Department “EO Data Science,” Remote Sensing Technology Institute, German Aerospace Center (DLR), Cologne, Germany. She received the master’s (M.Sc.) degree, doctor of engineering (Dr.-Ing.) degree, and “Habilitation” degree in signal processing from Technical University of Munich (TUM), Munich, Germany, in 2008, 2011, and 2013, respectively. She is also the IEEE Fellow.
University of Oxford
University of Southern California - Institute for Creative Technologies
Sensat LTD.
The Hong Kong Polytechnic University
The Hong Kong Polytechnic University
National University of Defense Technology
University of Birmingham
University of Oxford
University of Oxford
- Urban3D@ICCV2021: https://urban3dchallenge.github.io/2021 [Replay]
- Urban3D@ECCV2022: https://urban3dchallenge.github.io/2022 [Replay]