HiP3R: Hierarchical Pose Graphs for Long-Horizon 3D Reconstruction
HiP3R: Hierarchical Pose Graphs for Long-Horizon 3D Reconstruction
HiP3R uses hierarchical pose graphs to reconstruct and merge local videos into globally consistent camera trajectories and 3D geometry without retraining. It first optimizes robust block-level Sim(3) graphs to establish global layout and scale, then fixes this layout and estimates shared SE(3) cameras for each frame, converting overlapping window predictions into a single trajectory. Experiments on multiple benchmarks prove HiP3R's effectiveness in improving camera trajectories and 3D reconstruction, and it is applicable to both block-based and streaming backends without additional training or fine-tuning.
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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