LongStream: Long-Sequence Streaming Autoregressive Visual Geometry

📅 2026-02-13
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📝 Abstract
Long-sequence streaming 3D reconstruction remains a significant open challenge. Existing autoregressive models often fail when processing long sequences. They typically anchor poses to the first frame, which leads to attention decay, scale drift, and extrapolation errors. We introduce LongStream, a novel gauge-decoupled streaming visual geometry model for metric-scale scene reconstruction across thousands of frames. Our approach is threefold. First, we discard the first-frame anchor and predict keyframe-relative poses. This reformulates long-range extrapolation into a constant-difficulty local task. Second, we introduce orthogonal scale learning. This method fully disentangles geometry from scale estimation to suppress drift. Finally, we solve Transformer cache issues such as attention-sink reliance and long-term KV-cache contamination. We propose cache-consistent training combined with periodic cache refresh. This approach suppresses attention degradation over ultra-long sequences and reduces the gap between training and inference. Experiments show LongStream achieves state-of-the-art performance. It delivers stable, metric-scale reconstruction over kilometer-scale sequences at 18 FPS. Project Page: https://3dagentworld.github.io/longstream/
Problem

Research questions and friction points this paper is trying to address.

long-sequence streaming
3D reconstruction
autoregressive models
scale drift
attention decay
Innovation

Methods, ideas, or system contributions that make the work stand out.

gauge-decoupled
orthogonal scale learning
cache-consistent training
streaming 3D reconstruction
autoregressive visual geometry
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