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HorizonStream Transformer advances streaming 3D reconstruction

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have introduced HorizonStream, a novel Transformer-based architecture designed for long-horizon attention in streaming 3D reconstruction. This method addresses limitations in existing approaches that struggle with drift and jitter over extended sequences by explicitly factorizing geometric propagation as an evidence influence kernel. HorizonStream utilizes Geometric Linear Attention for multi-timescale evidence propagation and Geometric Local Attention with Spatiotemporal RoPE for reliable 3D matching, enabling stable reconstruction of sequences over 10,000 frames with constant memory and linear time. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Advances streaming 3D reconstruction capabilities, potentially improving applications in robotics and augmented reality.

RANK_REASON The cluster contains an academic paper detailing a new method for 3D reconstruction.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Chong Cheng, Peilin Tao, Nanjie Yao, Guanzhi Ding, Xianda Chen, Yuansen Du, Xiaoyang Guo, Wei Yin, Weiqiang Ren, Qian Zhang, Zhengqing Chen, Hao Wang ·

    HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction

    arXiv:2605.23889v1 Announce Type: new Abstract: Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods often suffer from drift, jitter, or collapse on long sequences. We trace these failures…

  2. arXiv cs.CV TIER_1 · Hao Wang ·

    HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction

    Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods often suffer from drift, jitter, or collapse on long sequences. We trace these failures to a fundamental mismatch. Streaming geometry i…

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