用VGGT构建低内存、时序一致的3D语义地图，适合辅助导航。
Building temporally coherent 3D maps with VGGT for memory-efficient Semantic SLAM
- 滑动窗口处理图像流，分块对齐子图降低内存占用
- 通过追踪头融合2D语义掩码生成3D物体，支持实时更新
- 保存时间戳与实例身份，实现环境变化检测，适合真实场景
我们提出一种基于视觉几何锚定变压器（VGGT）的快速时空场景理解框架。为实现3D场景表示的连续更新，采用滑动窗口处理图像流并分块对齐子图，有效缓解VGGT高内存需求。利用VGGT追踪头将2D语义实例掩码聚合为3D对象。通过存储时间戳与实例级身份信息，系统实现时序一致性与更丰富的上下文推理，支持环境变化检测。在知名基准数据集及专为辅助导航设计的自定义数据集上评估，结果表明该框架具备在真实场景中应用的能力。
原文摘要 · Abstract (English)
We present a fast, spatio-temporal scene understanding framework based on Visual Geometry Grounded Transformer (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including assistive navigation. To achieve continuous updates of the 3D scene representation, we process the image flow with a sliding window, aligning submaps, thereby overcoming VGGT's high memory demands. We exploit the VGGT tracking head to aggregate 2D semantic instance masks into 3D objects. To allow for temporal consistency and richer contextual reasoning the system stores timestamps and instance-level identities, thereby enabling the detection of changes in the environment. We evaluate the approach on well-known benchmarks and custom datasets specifically designed for assistive navigation scenarios. The results demonstrate the applicability of the framework to real-world scenarios.
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