barakplasma/lingbot-map-long-video

Long-video 3D reconstruction with LingBot-Map: video in, bounded GLB point cloud + camera trajectory out. Community Cog wrapper of Robbyant/lingbot-map.

Public
4 runs

Run barakplasma/lingbot-map-long-video with an API

Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.

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
The video to reconstruct into a 3D point cloud. For anything larger than a few " "seconds, pass a hosted URL rather than uploading the file directly.
sample_fps
number
5

Min: 0.25

Max: 30

How many frames per second to pull from the video. This is the main quality vs. " "speed dial: higher values give the model more overlap to work with (denser, more accurate " "geometry) but take longer and cost more. 5 suits a slow phone walkthrough; drop to 1-2 for " "fast-moving or very long footage.
max_points
integer
3000000

Min: 10000

Max: 8000000

Upper bound on the number of points in the full-resolution scene.glb. Higher means " "more detail and a larger file; lower keeps the download small. Points are spread evenly across " "the video so no section is starved.
max_preview_points
integer
50000

Min: 1000

Max: 300000

Detail of the in-browser preview (scene_preview.glb) that renders in Replicate's " "viewer. Each point becomes a small cube, so raising this sharpens the preview but enlarges the " "file and slows loading on phones. The full-resolution scene.glb is unaffected.
show_trajectory
boolean
True
Draw the camera's path through the scene as a colored tube in both GLB files.
confidence_percentile
number
60

Max: 99

Advanced. Discards this percentage of the least-confident points in every frame " "before export. Raise it to clean up noisy floaters and haze at the cost of some real geometry; " "lower it to keep more points including uncertain ones.
spatial_stride
integer
4

Min: 1

Max: 16

Advanced. Keeps every Nth pixel in each direction when turning depth maps into " "points (4 uses 1/16 of the pixels). Lower gives denser geometry and larger files; higher is " "faster and lighter. Works together with max_points, which is the final ceiling.
point_frame_stride
integer
2

Min: 1

Max: 30

Advanced. Uses every Nth sampled frame for point geometry (2 = every other frame). " "Camera poses are still recorded for every frame in trajectory.json; this only thins the points. " "Raise it to lighten very long videos.
max_frames
integer
12000

Min: 2

Max: 30000

Advanced. Hard safety cap on total frames processed, applied uniformly across the " "whole video. Acts as a backstop against runaway runtime on long footage; sample_fps usually " "reaches the limit first.
keyframe_interval
integer
2

Min: 1

Max: 30

Advanced. Persists every Nth frame in the model's KV cache. Larger values save " "memory on long, full-FPS footage (upstream suggests 8-12 there) at some accuracy cost; leave " "at 2 for ordinary clips.
window_size
integer
128

Min: 16

Max: 256

Advanced. KV-cache slots per inference window (upstream long-video recommendation " "is 128). Rarely needs changing unless you are tuning memory use.
overlap_keyframes
integer
8

Min: 2

Max: 32

Advanced. Number of keyframes shared between consecutive windows and macro-chunks " "so their poses and depths align. More overlap improves alignment continuity but adds compute.
chunk_frames
integer
512

Min: 128

Max: 1536

Advanced. Largest number of dense prediction frames held in memory at once. Lower " "it if a very long video runs out of memory; raise it for slightly better cross-chunk alignment " "when memory allows.

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{
  "type": "array",
  "items": {
    "type": "string",
    "format": "uri"
  },
  "title": "Output"
}