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Input schema
The fields you can use to run this model with an API. If you don’t give a value for a field its default value will be used.
| Field | Type | Default value | Description |
|---|---|---|---|
| video |
string
|
Source video. A slow, steady orbit around a static subject works best.
|
|
| reconstruction_method |
None
|
glomap
|
Structure-from-Motion method. glomap (default): fast global SfM, recommended. colmap: slower incremental, sometimes more robust on difficult captures.
|
| target_framecount |
integer
|
600
Min: 30 Max: 2000 |
Target number of frames to sample from the video.
|
| training_num_steps |
integer
|
30000
Min: 2000 Max: 30000 |
gsplat training steps. Fewer is faster but lower quality.
|
| training_max_num_gaussians |
integer
|
500000
Min: 100000 Max: 10000000 |
Cap on the number of Gaussians: the primary control on output detail and .ply size (~164 bytes/Gaussian, so 5M is ~820 MB). Higher means more detail and a larger file. The 500k default is a sensible mid-range; raise it for large or complex scenes. Warning: a high cap increases training time and may cause the job to time out depending on machine capacity.
|
| equirectangular |
boolean
|
False
|
Treat the input as 360/equirectangular video.
|
| use_background_sphere |
boolean
|
True
|
Add a background sphere for distant/sky content. Helps outdoor or 360 captures where the background is far away.
|
| remove_background |
boolean
|
False
|
Remove the background from each frame before reconstruction (InSPyReNet). Good for isolating a single object; leave off for scenes/environments where you want the surroundings.
|
| apriltag_size_meters |
number
|
0
Max: 10 |
Optional. If an AprilTag of known physical size (in meters) is visible in the video, set it to scale the reconstruction to real-world units. Leave at 0 to skip AprilTag detection entirely.
|
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{'format': 'uri', 'title': 'Output', 'type': 'string'}