vMAP: Vectorised Object Mapping for Neural Field SLAM, new at #CVPR2023! Each object is represented by a separate MLP, optimised in parallel via vectorised training. Full info: Dyson Robotics Lab, Imperial College, Shikun Liu, Marwan Taher, Andrew Davison.

2/n Neural field SLAM excels in 3D mapping but lacks control over the scene. Our vMAP system detects object instances on-the-fly, creating object-level representations for better reconstruction and enabling object tracking and scene editing.

5/n Code is available: We also include iMAP as a special case of vMAP when we assume the whole 3D scene as a single instance.

4/n Using neural field representation is beneficial because it's compact, fills unobserved parts naturally, and doesn't require extra priors. With our independent modeling, object-level completion performs better and can be integrated with 3D prior for a more complete model!

3/n Thanks to the powerful @functorch, we are able to train many small MLPs extremely efficiently in a batch, leading to much faster training speed compared to naive sequential for-loop operations.