CVPR Workshop Urban Scene Modeling 2024

Rapid urbanization poses social and environmental challenges. Addressing these issues effectively requires access to accurate and up-to-date 3D building models, obtained promptly and cost-effectively. Urban modeling is an interdisciplinary topic among computer vision, graphics, and photogrammetry. The demand for automated interpretation of scene geometry and semantics has surged due to various applications, including autonomous navigation, augmented reality, smart cities, and digital twins. As a result, substantial research effort has been dedicated to urban scene modeling within the computer vision and graphics communities, with a particular focus on photogrammetry, which has coped with urban modeling challenges for decades. This workshop is intended to bring researchers from these communities together. Through invited talks, spotlight presentations, a workshop challenge, and a poster session, it will increase interdisciplinary interaction and collaboration among photogrammetry, computer vision and graphics. We also solicit original contributions in the areas related to urban scene modeling.

6 Keynotes
Full Day Workshop
2 Competition Tracks

News!

Feb 15, 2024: Building3D Challenge Competition Begins! 🎉

Feb 8, 2024: USM3D 2024 Call for Papers released!

Feb 7, 2024: CVPR Workshop list released!

Feb 3, 2024: We are live!

Call for Papers

Important Dates

Papers (in proceedings)

  • Submission Deadline: March 24, 2024
  • Notification of Acceptance: April 9, 2024
  • Camera Ready Deadline: April 12, 2024

========> Submit Here <========

Topics

The goal of this workshop is to push the frontier in urban scene modeling. Focal points for papers include but are not limited to:
  • Semantic/instance segmentation of 3D point clouds and images on urban scenes
  • Scene representation: Neural implicit scene representation, SDF, NeRF, Gaussian splats, mesh, CAD, etc.
  • 2.5D/3D reconstruction and modeling from remote sensing data (satellite images, LiDAR, etc.)
  • Generative models: Diffusion models and GANs for occlusion-free image and 3D scene generation
  • Meta learning for image/point cloud registration, segmentation, and modeling
  • Self-, weakly, and semi-supervised learning for urban scene modeling
  • Fusion of images, point clouds and other sensor data for urban scene modeling
  • Rendering and visualization of large-scale point clouds
  • Parametric reconstruction
  • 3D reconstruction and registration, joint registration and segmentation of images, matching multi view, pose estimation, deployment on mobile and embedded devices
  • Leveraging (learned) priors for structured/parametric 3D reconstruction from multimodal data
  • New datasets, and new labeling, and capturing methods for ground truth
  • Human-in-the-loop modeling and reconstruction
  • Methods to bridge traditional modeling tools with neural representations
  • Inverse and forward procedural modeling approaches for large scale scene generation
  • Novel contributions on depth, panoramic, and other image representations for scene understanding, modeling, and reconstruction
We welcome PC self-nominations. If you're willing to review for the workshop, please reach out to us at usm3d@googlegroups.com.

Challenges

Building3D Challenge!

Building3D
Challenge!

Building3D Challenge

As part of this workshop, we are hosting the Building3D challenge. Building3D is an urban-scale publicly available dataset consisting of more than 160 thousand buildings with corresponding point clouds, meshes, and wireframe models covering 16 cities in Estonia. For this challenge, approximately 36, 000 buildings from the city of Tallinn are used as the training and testing dataset. Among them , we selected 6000 relatively dense and structurally simple buildings as the Entry-level dataset. The wireframe model is composed of points and edges representing shape and outline of the object. We require algorithms to take the original point cloud as input and regress the wireframe model. For the evaluation, the metrics of mean precision and recall are employed to evaluate accuracy of both points and edges, and overall offset of the model is calculated. Additionally, the wireframe edit distance (WED) is used as an additional metric to evaluate the accuracy of generated wireframe models.

Awards & Submissions

The winning submission will receive a cash prize provided by the workshop sponsor and the chosen finalists will be invited to present their research in the workshop. The prerequisite to receive a money prize is to provide a write-up detailing their solution by a submission to the workshop, in the form of an extended abstract (4 pages) or a full paper (8 pages), as well as the code required to generate a winning submission under CC BY4.0 license.

Important Dates

Feb 15 2024, Th:       Competition starts
May 31 2024, Fri:      Competition ends
June 7 2024, Fri:        Notification to Participants

Keynotes

Coming Soon!

Coming
Soon!

Organizers

Ruisheng Wang

Ruisheng
Wang

Professor
University of Calgary / Shenzhen University

Jack Langerman

Jack
Langerman

Sr. Applied Scientist
HOVER Inc.
 

Ilke Demir

Ilke
Demir

Senior Research Scientist
Intel Corporation
 

Qixing Huang

Qixing
Huang

Associate Professor
University of Texas at Austin

Florent Lafarge

Florent
Lafarge

Researcher
INRIA
 

Dmytro Mishkin

Dmytro
Mishkin

Researcher
Czech Technical University in Prague

Tolga Birdal

Tolga
Birdal

Assistant Professor
Imperial College London, UK

Hui Huang

Hui
Huang

Professor
Shenzhen University
 

Shangfeng Huang

Shangfeng
Huang

Researcher
University of Calgary
 

Daoyi Gao

Daoyi
Gao

Researcher
Technical University of Munich

Xiang Ma

Xiang
Ma

Head of Research
Amazon Web Services
 

Hanzhi Chen

Hanzhi
Chen

Researcher
Technical University of Munich

Clement Mallet

Clement
Mallet

Research Scientist
LASTIG

Caner Korkmaz

Caner
Korkmaz

Researcher
Imperial College London

Yang Wang

Yang
Wang

Associate Professor
Concordia University

Marc Pollefeys

Marc
Pollefeys

Professor
ETH Zurich

Program Committee

Name Affiliation
Daniel G. Aliaga Purdue University
Daniela Cabiddu CNR - Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI)
Yiping Chen Sun Yat-Sen University
Ian Endres Hover Inc.
Hongchao Fan Norwegian University of Science and Technology
Antoine Guédon École Des Ponts Paristech
Liu He Purdue University
Qingyong Hu National University of Defense Technology
Yuzhong Huang Hover Inc. / USC Information Sciences Institute
Loic Landrieu Ecole des Ponts ParisTech
David Marx CoreWeave, EleutherAI
Philippos Mordohai Stevens Institute of Technology
Name Affiliation
Liangliang Nan Delft University of Technology
Rongjun Qin The Ohio State University
Ziming Qui NYU / Lowe's
Gunho Sohn York University
Ioannis Stamos City University of New York
Gábor Sörös Bell Labs
George Vosselman University of Twente
Jun Wang Nanjing University of Aeronautics and Astronautics
Chenglu Wen Xiamen University
Michael Ying Yang University of Bath
Bo Yang The Hong Kong Polytechnic University
Wei Yao The Hong Kong Polytechnic University

This is a CVPR 2024 workshop