This workshop aims to explore the role and potential of Neural Fields i.e. in various robotics domains, including 6D object pose estimation, SLAM, manipulation with reinforcement learning (RL), object reconstruction, neural implicit data generation, few-view scene reconstruction, camera calibration, physics modeling, and planning/navigation. By leveraging recent advancements in computer vision, such as neural radiance fields (NeRFs) and deep Signed Distance Functions (DeepSDFs), this workshop aims to foster discussions and collaborations in the robotics community.
Through invited and selected spotlight talks as well as a room for discussions, the workshop will address several key topics:
- The advantages of neural fields beyond reconstruction for the robotics context
- Trade-offs between an implicit and explicit representation
- Online training of neural fields and the speed/efficiency trade-offs
- Balancing fully-implicit representations which are less interpretable with hybrid neural field based representations which offer more interpretability
By facilitating knowledge exchange and exploration, we hope the workshop sparks interest in neural fields for robotics, shaping the future of the field and driving innovation in perception, planning, and control systems.