arXiv:2606.27818v1 Announce Type: cross Abstract: We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points. We model registration as a nonlinear least-squares problem based on the Maximum Mean Discrepancy, approximated using random Fourier features. The resulting objective can be solved efficiently with standard methods such as Levenberg-Marquardt, and the solution is differentiable via the implicit function theorem. This allows MMD-Reg to be used as a differentiable optimization
The continuous drive for more efficient and robust perception systems in AI, particularly for robotics and autonomous systems, necessitates advancements in fundamental capabilities like point-cloud registration.
This development offers a differentiable and computationally efficient method for point-cloud registration, a critical component for AI models learning in 3D environments, enabling more robust interaction with the physical world.
The ability to integrate point-cloud registration directly into end-to-end differentiable learning pipelines will simplify development and improve performance for systems that rely on 3D data.
- · Robotics companies
- · Autonomous vehicle developers
- · AI researchers in 3D vision
- · Industrial automation
- · Legacy 3D registration methods
- · Software reliant on non-differentiable 3D processing
Improved accuracy and speed of 3D environment perception in autonomous systems.
Accelerated development of general-purpose humanoid robots and advanced manufacturing processes.
Enhanced AI agents operating in complex physical environments, leading to higher levels of autonomy.
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Read at arXiv cs.LG