Amortized Anchor Refinement for Deployable Continuous-Time 4D Gaussian Reconstruction

2026-08-31

AI Summary

为解决独立XR头显上的连续时间4D重建问题，提出了一种通过冻结主干网络预测初始高斯表示并进行短时优化的方法，同时应用持久同调约束来修剪不稳定高斯。

Abstract

Continuous-time 4D reconstruction remains impractical on standalone XR headsets. Per-scene optimization demands deployment-infeasible compute, and lower budgets cause collapse rather than degrade gradually. Feed-forward prediction is fast, but struggle to recover scene-specific detail. We present Amortized Anchor Refinement, which uses a frozen backbone to predict an initial Gaussian representation and a short optimization to specialize it under a fixed compute budget, with a capacity floor preserving representational density. A training-free stage then applies a persistent-homology constraint to prune unstable Gaussians while preserving topologically persistent structures, and streams the resulting trajectories directly as scene flow. On the Stage-Capture benchmark, Amortized Anchor Refinement achieves 24.31$\pm$2.22dB, while our deployment experiments demonstrate reconstruction within the target budget on a single consumer GPU and playback on a standalone XR headset.

Problem

• Continuous-time 4D reconstruction
• XR headsets
• Per-scene optimization
• Feed-forward prediction
• Scene-specific detail

Innovation

• Amortized Anchor Refinement
• Gaussian Representation
• Persistent Homology
• Scene Flow
• Continuous-time 4D Reconstruction