RePose-NeRF: Robust Radiance Fields for Mesh Reconstruction under Noisy Camera Poses

The publication of 'RePose-NeRF' marks a significant advancement in the field of 3D reconstruction, which is crucial for various robotic tasks such as navigation and environment understanding. Traditional methods struggle with the accuracy of camera poses, especially in real-world scenarios, limiting their effectiveness. The proposed framework overcomes these challenges by jointly refining camera poses while learning an implicit scene representation, resulting in high-quality, editable 3D meshes. These meshes are compatible with common 3D graphics and robotics tools, bridging the gap between neural implicit representations and practical applications. Experiments on standard benchmarks confirm the method's robustness and accuracy under pose uncertainty, highlighting its potential to enhance robotic capabilities.