LingBot-Map Tutorial Details 3D Reconstruction Pipeline

A comprehensive tutorial has been released detailing the implementation of an end-to-end streaming 3D reconstruction pipeline using LingBot-Map. This tutorial guides users through the entire process, starting with the initial configuration of input sources, reconstruction settings, selection of pre-trained checkpoints, and output controls. A key feature highlighted is the pipeline's ability to probe available GPU resources and automatically tune various parameters based on detected VRAM. These tunable parameters include frame limits, camera iterations, scale frames, and KV-cache settings, ensuring optimal performance for the specific hardware.

The tutorial covers the installation of the LingBot-Map repository and its dependencies, followed by the download of a pre-trained checkpoint. Users are then instructed on how to preprocess image or video frames before constructing the GCTStream model. This model incorporates streaming attention and long-range trajectory memory, crucial for maintaining context and accuracy in dynamic 3D reconstruction scenarios. The process involves performing mixed-precision inference, where the model processes data with reduced precision to accelerate computation while minimizing accuracy loss.

Following inference, the tutorial explains how to decode the predicted camera poses and intrinsic parameters. These decoded parameters are then used to convert depth maps into world-coordinate point clouds, effectively transforming 2D depth information into a 3D representation. The recovered geometry is validated to ensure its accuracy and fidelity. Subsequently, users can visualize the reconstructed scene and the camera trajectory, providing an intuitive understanding of the reconstruction process and its output. The tutorial concludes by detailing how to export the results in various common 3D file formats, including PLY, NPZ, and GLB artifacts, facilitating further use in other 3D applications or analysis tools.

Specific configuration options detailed in the tutorial include setting the scene to 'courthouse', specifying image or video paths, defining frames per second (fps) at 10, and managing maximum frames and stride. The checkpoint file used is 'lingbot-map.pt', with an image size of 518 and a patch size of 14. The tutorial also mentions the use of 'use_sdpa' for attention mechanisms and operates in 'streaming' mode. For memory management, a KV-cache sliding window of 64 is specified, and offloading to CPU is an option. The output point cloud can be limited to a maximum of 60,000 points, and export options include PLY and GLB formats.