Awesome-Implicit-NeRF-Robotics
About the Project
The Awesome-Implicit-NeRF-Robotics repository is a comprehensive collection of important papers related to implicit representations and Neural Radiance Fields (NeRF) within the realm of Robotics and Reinforcement Learning (RL). Inspired by similar resources like the awesome-computer-vision list, this repository serves as a central hub for anyone interested in the intersection of these cutting-edge technologies and robotics.
The repository is actively maintained, welcoming contributions from the community through pull requests or emails to add relevant papers. It's a dynamic space where the latest advancements are continuously added, and users are encouraged to star the repository or cite it if they find it beneficial.
For those new to NeRFs, the repository suggests some foundational materials like survey papers, blog posts, and collections, providing an excellent starting point to understand NeRFs thoroughly.
Recent News
The repository highlights significant milestones and recent developments, such as:
- Release of a comprehensive survey paper, "Neural Fields in Robotics: A Survey," which explores the role of neural fields in robotics.
- Conduction of the first Workshop on Neural Fields in Robotics at the International Conference on Robotics and Automation (ICRA) 2024 in Yokohama, Japan.
Core Areas of Focus
The repository organizes its resources around key areas of research and application, each representing a sub-domain where implicit representations and NeRFs have transformative potential:
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Object Pose Estimation: Techniques and papers focusing on accurately determining the position and orientation of objects using advanced NeRF methods.
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SLAM (Simultaneous Localization and Mapping): Research related to building and updating maps of an environment while keeping track of an agent's location within it using neural representations.
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Manipulation/RL: Papers that explore the integration of NeRFs with robotic manipulation and reinforcement learning, enhancing robots' interaction abilities with their environments.
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Object Reconstruction: Advanced methods that utilize implicit representations to recreate detailed 3D models of objects from observed data.
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Physics: Application of NeRFs and implicit representations in understanding and simulating physical phenomena in robotic systems.
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Planning/Navigation: Utilizing neural fields to enhance the planning and navigational capabilities of robotic systems.
Community Involvement
The project strongly encourages community engagement in its growth and development. By inviting contributions, discussions, and dissemination of knowledge, the repository serves as a valuable resource for researchers and practitioners in the field. It supports the sharing of ideas and collective advancement in the understanding and application of NeRF and implicit representations in robotics.
Conclusion
Awesome-Implicit-NeRF-Robotics is more than just a list; it's a curated, continuously updated repository reflecting the dynamic intersection of robotics and the latest advancements in neural representations. Whether you are a researcher, student, or practitioner in robotics or AI, this project provides a robust foundation to delve deeper into NeRF and its versatile applications in robotics.