# Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion

## TL;DR

This paper introduces GMC, a novel method for 3D scene interpolation that effectively handles large motions by learning SE(3) mappings, resulting in smoother transitions and better extrapolation compared to existing methods.

## Contribution

GMC is the first approach to robustly interpolate 3D scenes with large global motions by learning unary potential fields into a shared canonical space.

## Key findings

- Outperforms existing methods on large-motion scene interpolation
- Enables extrapolation beyond observed states
- Achieves smoother and more accurate scene transitions

## Abstract

Existing dynamic scene interpolation methods typically assume that the motion between consecutive timesteps is small enough so that displacements can be locally approximated by linear models. In practice, even slight deviations from this small-motion assumption can cause conventional techniques to fail. In this paper, we introduce Global Motion Corresponder (GMC), a novel approach that robustly handles large motion and achieves smooth transitions. GMC learns unary potential fields that predict SE(3) mappings into a shared canonical space, balancing correspondence, spatial and semantic smoothness, and local rigidity. We demonstrate that our method significantly outperforms existing baselines on 3D scene interpolation when the two states undergo large global motions. Furthermore, our method enables extrapolation capabilities where other baseline methods cannot.