用固定算力实现可部署的连续时空4D高斯重建
Amortized Anchor Refinement for Deployable Continuous-Time 4D Gaussian Reconstruction
- 先用冻结主干预测初始高斯表示，再短时优化适配场景
- 在消费级显卡上完成重建，实现在独立XR头显播放
- 通过拓扑约束去除不稳点，保留关键结构动态
连续时间4D重建在独立XR头显上仍不实用。每场景优化需要超出部署能力的计算资源，预算降低会导致重建崩溃而非渐变退化。前馈预测虽快，但难以恢复特定场景细节。本文提出逐次锚点精炼（Amortized Anchor Refinement），利用冻结主干网络预测初始高斯表示，并在固定计算预算下通过短时优化进行场景特化，同时设置容量下限以维持表示密度。随后训练无关阶段应用持久同调约束，剔除不稳定高斯点，同时保留具有拓扑持续性的结构，并直接输出轨迹作为场景光流。在Stage-Capture基准上，该方法达到24.31±2.22dB性能；部署实验表明，可在单张消费级显卡上完成重建，并在独立XR头显上实时播放。
原文摘要 · Abstract (English)
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.
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