HiP3R：用于长时程 3D 重建的层次位姿图
HiP3R: Hierarchical Pose Graphs for Long-Horizon 3D Reconstruction
HiP3R 使用层次位姿图将局部视频重建合并为全局一致的相机轨迹和 3D 几何，无需重新训练。它首先优化鲁棒的块级 Sim(3) 图以建立全局布局和尺度，然后固定此布局并估计每帧的共享 SE(3) 相机，将重叠窗口预测转换为单一轨迹。实验在多个基准上证明 HiP3R 在改进相机轨迹和 3D 重建方面的有效性，且适用于基于块和流式后端，无需额外训练或微调。
Published on Oct 22
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Abstract
HiP3R uses a hierarchical pose graph to merge local video reconstructions into globally consistent camera trajectories and 3D geometry without retraining.
Feed-forward models accurately estimate camera motion and 3D geometry from short video windows. However, computational and memory costs prevent direct inference on sequences containing thousands of frames. Long videos must therefore be processed as overlapping chunks. This division produces independent local reconstructions with inconsistent coordinate frames and duplicate estimates of shared cameras. Achieving global consistency requires propagating constraints from adjacent overlaps and distant revisits across the sequence. This requirement naturally motivates pose graph optimization. However, the choice of graph level creates a trade-off. Chunk-level graphs efficiently resolve scale drift and close large loops. Yet, their rigid transformations cannot correct internal errors or conflicting frame predictions. Conversely, frame-level optimization offers local flexibility, but scaling it globally is computationally expensive and can break metric scale. HiP3R resolves this trade-off using a hierarchical pose graph. HiP3R first optimizes a robust chunk-level Sim(3) graph to establish the global layout and scale. It then holds this layout fixed and estimates one shared SE(3) camera per frame, turning overlapping window predictions into a single trajectory. Experiments across multiple benchmarks demonstrate the effectiveness of HiP3R in improving camera trajectories and 3D reconstruction. Our method applies to both chunk-based and streaming backends without additional training or fine-tuning. The codes are available at https://kkakkkka.github.io/HiP3R.
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