Geometric Context Transformer for Streaming 3D Reconstruction
Authors: name: Lin-Zhuo Chen, name: Jian Gao, name: Yihang Chen, name: Ka Leong Cheng, name: Yipengjing Sun, name: Liangxiao Hu, name: Nan Xue, name: Xing Zhu, name: Yujun Shen, name: Yao Yao, name: Yinghao Xu
Organization: Robbyant

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.