Wild 3D

3D Modeling, Reconstruction, and Generation in the Wild

in conjunction with ECCV 2024, Milan, Italy.

Time: September 30th, 2024.
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Overview

The goal of this workshop is to bring together researchers who are studying approaches to reconstructing and understanding 3D models in challenging, real-world environments. While significant progress has been made in the field of multi-view 3D modeling, many existing methods are limited by assumptions of controlled acquisition, such as static scenes and densely captured viewpoints. On the other hand, advances in 3D learning and the widespread availability of 2D and 3D visual data offer new opportunities to overcome these challenges and make 3D modeling more robust, widely applicable, easily accessible, and cost effective. This workshop aims to address the challenges of 3D modeling in uncontrolled, partially-observed, and noisy environments and to explore potential future directions in the field. By fostering communication and highlighting important work in this area, we hope to inspire new research topics and breakthroughs

Schedule (tentative)

09:00-09:10 Opening Remarks
09:10 - 09:35 Invited Talk 1
09:35 - 10:10 Invited Talk 2
10:10 - 10:25 Oral Presentation 1
10:25 - 10:55 Poster Session and Coffee Break
10:55 - 11:10 Oral Presentation 2
11:10 - 11:35 Invited Talk 3
11:35 - 12:00 Invited Talk 4

Related Workshops

  • 3D Scene Understanding Workshop covers the broad scope of 3D scene understanding, while our proposed workshop dive deeper into the challenges and methodologies of 3D modeling and reasoning under in-the-wild, challenging, and sometimes extreme setup.
  • Long-Term Visual Localization under Changing Conditions focuses on the task of visual localization. Our workshop takes a broader lens, covering the challenges and applications across various 3D computer vision tasks.
  • Image Matching: Local Features & Beyond focuses specifically on the task of cross-view feature matching. While 3D modeling and alignment is one of our topics of interest, our focus is broader. Additionally, instead of the traditional, classic setup, we are interested in challenging cases such as sparse viewpoints with small overlaps.
  • Learning 3D with Multi-View Supervision explores multi-view cues for 3D deep learning for various tasks, including recognition, detection, segmentation, and generation. While our workshop also concerns these techniques and tasks, we focus more on how to generalize these algorithms to challenging and extreme real-world setup.
  • Workshop on Computer Vision in the Wild covers a wide range of topics in computer vision, while we focus on studying the challenges and impact of existing algorithms and systems through the lens of 3D.

Call For Paper

We accept either 8-page extended abstracts or 14-page full papers submissions, excluding reference. The workshop papers are non-archival and we welcome submissions that were already submitted/accepted to other venues. All submissions have to follow the ECCV 2024 author guidelines.
  • Submission Portal: OpenReview
  • Paper Submission Deadline: August 15, 2024, 23:59:59 PST
  • Notification to Authors: September 1, 2024
  • Camera-ready submission: September 7, 2024

Topics of Interest

  • Alignment: How can we align observations subject to significant variations in appearance, lighting, contents, and viewpoints, and how can we register images with little or no overlap?
  • Modeling: How can we construct accurate 3D models from limited, noisy, and incomplete observations?
  • Representation: What are the most suitable representations for 3D modeling and reasoning that combine data-driven priors and limited observations? Do we need 3D representations, or would systems for view synthesis suffice?
  • Knowledge and Reasoning: How can we incorporate common sense knowledge of 3D objects and scenes, such as part structures, arrangements of objects, physical stability, and affordance, and how can we represent, learn, and encode this knowledge for 3D reasoning?
  • Datasets and Benchmarks: What datasets and benchmarks are crucial to validate the effectiveness of 3D algorithms in the wild? And what is currently lacking in this area?
  • Risks and ethical considerations: How can we mitigate the risks of these robust 3D modeling and reasoning techniques? How do we address relevant ethical questions, such as invasion of privacy and spreading misinformation.
  • Applications: What type of applications can we enable by developing more robust 3D algorithms? What type of modifications should be made? Are there other new and exciting applications for in-the-wild 3D modeling in fields such as construction, agriculture, and remote sensing?