Geometric Context Transformer for Streaming 3D Reconstruction
Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal
  consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation
  model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully
  designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and
  long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around
  20 FPS on 518 x 378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach
  achieves superior performance compared to both existing streaming and iterative optimization-based approaches.
69e090265b704445ba50b80b
Lin-Zhuo Chen
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Jian Gao
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Yihang Chen
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Ka Leong Cheng
admin_assigned
2026-04-16T20:26:44.373Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64acd2ec39fcfebff8c79c00/Avq66l5hO-aggNtk4Y1ss.png
Ka Leong Cheng
felixcheng97
user
felixcheng97
69e090265b704445ba50b80f
Yipengjing Sun
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Liangxiao Hu
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Nan Xue
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Xing Zhu
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Yujun Shen
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Yao Yao
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Yinghao Xu
Geometric Context Transformer for Streaming 3D Reconstruction
Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal
  consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation
  model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully
  designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and
  long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around
  20 FPS on 518 x 378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach
  achieves superior performance compared to both existing streaming and iterative optimization-based approaches.
LingBot-Map is a feed-forward 3D foundation model that reconstructs scenes from video streams using a geometric context transformer architecture with specialized attention mechanisms for coordinate grounding, dense geometric cues, and long-range drift correction, achieving stable real-time performance at 20 FPS.
Simultaneous Localization and Mapping feed-forward 3D foundation model geometric context transformer attention mechanism anchor context pose-reference window trajectory memory coordinate grounding dense geometric cues long-range drift correction