π3-LEGS: a lightweight and fast geometry system for long- sequence 3D reconstruction
In recent years, Transformer-based 3D vision foundation models have demonstrated strong generalization in multiview geometry and scene reconstruction. However, their scalability to long-sequence, urban-scale RGB streams remains limited due to the quadratic complexity of attention mechanisms, redundant frame processing, and high GPU memory pressure caused by dense spatial tokens. Although VGGT-Long partially alleviates these issues through chunked inference and loop-closure optimization, its geometric reasoning pipeline still involves substantial redundant computation, making it difficult to balance efficiency and accuracy in long-sequence scenarios. In this paper, we revisit the computational bottlenecks in long-sequence geometric inference, with a particular focus on spatial token redundancy and cross-frame global attention. We propose π³-LEGS, an efficient geometric inference system designed for scalable long-sequence 3D reconstruction. π³-LEGS integrates three key components: (1) a π³-based permutation-equivariant inference module to enhance unordered multi-view feature aggregation; (2) a geometry-aware keyframe selection mechanism that dynamically filters low-contribution frames to reduce redundant computation; and (3) a training-free block-sparse attention strategy that adaptively generates sparse attention masks based on pooled Query–Key similarity, significantly reducing global attention overhead. Extensive experiments on the KITTI Odometry dataset demonstrate that π³-LEGS achieves an average Absolute Trajectory Error (ATE) of 26.51, improving upon VGGT-Long by 6.4%, while reducing end-to-end inference time by 15.9%. Moreover, the proposed system maintains stable performance on thousand-frame sequences without runtime failures, highlighting its effectiveness in achieving a favorable accuracy efficiency trade-off for large-scale 3D reconstruction.