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.
The 1st Challenge on Large Scale Point-cloud Analysis for Urban Scenes Understanding (Urban3D) at ICCV 2021 aims to establish a new benchmark for 3D semantic segmentation on urban-scale point clouds. In particular, we prime the challenge with a dataset, called SensatUrban, which 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, the SensatUrban dataset allows us to explore a number of key research problems and directions for 3D semantic learning in this workshop. We aspire to highlight the challenges faced in 3D semantic 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 dataset, and this workshop could inspire the community to explore the next level of 3D semantic 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 supervised learning in 3D point clouds analysis.
- Learning from imbalanced 3D point clouds.
- 3D point cloud acquisition & visualization.
- 3D object detection & reconstruction.
- Semi-/weak-/un-/self- supervised learning methods for 3D point clouds.
Call for Contributions
The Urban3D Challenges are hosted on Codalab, and can be found at:
- SensatUrban dataset: http://point-cloud-analysis.cs.ox.ac.uk/
- SensatUrban API: https://github.com/QingyongHu/SensatUrban
- Urban3D Challenge: https://competitions.codalab.org/competitions/31519
More information on the respective challenges can be found on their pages.
Preliminary Program Outline
|08:35-09:20||Invited Talk (Talk 1)|
|09:20-10:05||Invited Talk (Talk 2)|
|10:20-10:40||Winner Talk (Track 1) + Q&A|
|10:40-11:00||Winner Talk (Track 2) + Q&A|
|11:00-11:20||Winner Talk (Track 3) + Q&A|
Invited Keynote Speakers
Konrad Schindler received the Diplomingenieur (M.Tech.) degree from Vienna University of Technology, Vienna, Austria, in 1999, and the Ph.D. degree from Graz University of Technology, Graz, Austria, in 2003. He was a Photogrammetric Engineer in the private industry and held researcher positions at Graz University of Technology, Monash University, Melbourne, VIC, Australia, and ETH Zurich, Zurich, Switzerland. He was an Assistant Professor of Image Understanding with TU Darmstadt, Darmstadt, Germany, in 2009. Since 2010, he has been a Tenured Professor of Photogrammetry and Remote Sensing with ETH Zurich. His research interests include computer vision, photogrammetry, and remote sensing. He is the owner of the Semantic3D.net benchmark.
University of Toronto
Raquel Urtasun is Uber ATG Chief Scientist and the Head of Uber ATG Toronto. She is also a Full Professor in the Department of Computer Science at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. Her research interests include machine learning, computer vision, robotics, AI and remote sensing. Her lab was selected as an NVIDIA NVAIL lab. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, two NVIDIA Pioneer Research Awards, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded at CVPR in 2013 and 2017 respectively. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto's top influencers by Adweek magazine.