Downstream Pipelines
Downstream Pipelines
A splatpack is reconstruction-ready input. This guide outlines how to take one into a radiance field reconstruction. The right path depends on which mode you captured in.
Pick your path
Choose the mode you captured in to see the recommended route.
Use the on-device COLMAP model — skip structure-from-motion entirely.
- Locate COLMAP_Text_Model/ inside your LidarSeries_<date> folder.
- Point your Gaussian splatting trainer at the model — images under images/, sparse model under sparse/0/.
- Poses come straight from ARKit: fast and deterministic, no feature matching.
LiDAR → Gaussian splatting (fastest path)
LiDAR captures include an on-device COLMAP model (COLMAP_Text_Model/) — camera poses, images, and a sparse point cloud — so you can skip structure-from-motion entirely.
- Locate COLMAP_Text_Model/ inside your LidarSeries_<date> folder.
- (Optional) Inspect it in COLMAP: File → Import model, with the image path set to COLMAP_Text_Model/images/.
- Point your Gaussian splatting trainer at the model — images under images/, the sparse model under sparse/0/.
Because the poses come straight from ARKit, this path is fast and deterministic — no feature matching, no SfM solve.
Photo or Video → COLMAP → Gaussian splatting
Photo and video captures don’t carry camera poses, so you reconstruct them the standard way: recover poses with COLMAP, then train. For video, extract frames from wide.mov and ultra.mov first (using both lenses gives broader coverage); for photo, use the wide_* and ultra_* images directly.
- Feature extraction
- Feature matching
- Mapping (structure-from-motion) → a sparse model
- Take the sparse model + images into your Gaussian splatting trainer.
COLMAP’s Automatic Reconstruction (GUI) or colmap automatic_reconstructor (CLI) runs steps 1–3 in one pass.
LiDAR point cloud → CloudCompare
To inspect or use the fused LiDAR point cloud directly, open lidar_pointcloud_world_xyz.ply in CloudCompare. See the CloudCompare guide for details.