VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction

2026-08-15

AI Summary

This study addresses the loss of global consistency caused by scale drift in long-sequence 3D reconstruction by proposing the SGIA framework. The method introduces a novel scene geometry invariant anchoring mechanism that degrades Sim(3) alignment to rigid-body transformation, thereby interrupting error propagation chains. Furthermore, a lightweight test-time adaptation strategy is designed to optimize local predictions through multi-objective self-supervision and normalization layer fine-tuning, enhancing reconstruction accuracy without retraining. Experiments demonstrate that SGIA achieves state-of-the-art performance across multiple long-sequence benchmarks, reducing absolute trajectory error by up to 32%. These results indicate significant improvements in both trajectory stability and global geometric consistency for large-scale reconstruction tasks.

Abstract

Maintaining global geometric consistency is a central challenge in long-sequence 3D reconstruction, with scale drift being the most critical failure mode. In chunk-based inference pipelines, the scale degree of freedom in sequential Sim(3) alignment is left unconstrained, causing estimation errors to compound multiplicatively and distort global trajectories and point cloud geometry. We present a scale-consistency enhancement framework built on a key insight: in structured environments such as driving scenes, geometric quantities arising from environmental regularity remain inherently invariant across temporal segments, and discrepancies in their per-chunk measurements directly expose inter-chunk scale drift. We propose Scene Geometric Invariant Anchoring (SGIA), which extracts dominant geometric invariants from each chunk's predicted point cloud via coarse-to-fine robust estimation and exploits their cross-chunk consistency to establish scale constraints independent of point cloud registration, explicitly degenerating 7-DoF Sim(3) alignment into 6-DoF rigid-body transformation and severing chain-wise scale error propagation at its source. We further introduce a lightweight test-time adaptation strategy that fine-tunes only normalization-layer parameters via multi-objective self-supervision, progressively improving intra-chunk predictions along the sequence. Both modules are plug-and-play and require no offline retraining. Experiments on multiple long-sequence benchmarks demonstrate state-of-the-art performance, reducing absolute trajectory error by up to 32% with significant gains in trajectory stability and reconstruction quality. Code: https://github.com/WZ-CS/VGGT-Align

Problem

• Long-sequence 3D Reconstruction
• Global Geometric Consistency
• Scale Drift
• Sim(3) Alignment

Innovation

• Scale Consistency
• Scene Geometric Invariant Anchoring
• Test-Time Adaptation
• Long-Sequence 3D Reconstruction
• Sim(3) Alignment