Exploring the World of Awesome Radiance Field-based 3D Editing
Introduction
The world of 3D computer vision and graphics is evolving rapidly, and one of the latest advancements in this field is Radiance Fields Editing. This technique, together with NeRF (Neural Radiance Fields) and 3D Gaussian Splatting (3D-GS), is set to revolutionize how we perceive and design 3D environments. The Awesome Radiance Field-based 3D Editing project serves as a comprehensive resource for those interested in mastering these cutting-edge technologies. Curated by esteemed experts from AIR-DISCOVER Lab, Université de Montréal, and Mila, this project provides an organized collection of seminal papers, recent research, and related surveys.
What is Radiance Field-based Editing?
Radiance Fields Editing refers to the process of manipulating and enhancing 3D models using radiance field techniques. The core idea is to represent 3D scenes as radiance fields, a method that synthesizes high-quality images from multiple viewpoints. NeRF, one of the pioneering approaches in this space, allows for the creation of photorealistic views of a 3D scene by capturing its volumetric properties.
Key Features of the Project
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Curated Resources: The project compiles significant papers and surveys relevant to Radiance Fields Editing, ensuring that users have access to pioneering and influential works.
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Community Engagement: Users are encouraged to contribute to the list by suggesting missing papers or updates, fostering a collaborative environment.
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Up-to-date Information: Regular updates on paper acceptance statuses ensure that the resource remains current with the latest advancements in the field.
Seminal Papers
Some foundational papers highlighted include:
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV 2020)
- 3D Gaussian Splatting for Real-Time Radiance Field Rendering (SIGGRAPH 2023)
These works lay the groundwork for understanding the technical underpinnings of radiance field-based 3D scene synthesis and editing.
Related Surveys and Recent Research
The project also offers a wide array of surveys and papers detailing recent advancements. Topics include Gaussian splatting techniques, neural stylization, and multimodal image synthesis. For instance:
- 3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities
- Advances in 3D Neural Stylization: A Survey
Recent research papers cover innovative ideas like physics-based interactions with 3D objects, text-guided scene editing, and real-time rendering, reflecting the dynamic nature of the field.
Conclusion
The Awesome Radiance Field-based 3D Editing project is a must-visit for anyone interested in the future of 3D visualization and creative design. By providing access to a wealth of information and fostering an inclusive community of contributors, it empowers users to delve into the intricacies of Radiance Fields Editing and stay at the forefront of technological innovation.