# Robbyant/lingbot-map

A feed-forward 3D foundation model for reconstructing scenes from streaming data

Repository: https://github.com/Robbyant/lingbot-map
Canonical: https://ross.abutalabs.com/products/lingbot-map
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-21T17:37:56+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 10
- inputs: {"age_days": 140, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 16705, forks 1864 (observed 2026-08-28T04:11:17.014120+00:00)

## What it is
LingBot-Map is a feed-forward 3D foundation model that reconstructs scenes from streaming image data using a Geometric Context Transformer. It performs real-time streaming 3D reconstruction at roughly 20 FPS over sequences exceeding 10,000 frames, with paged KV cache attention for long-sequence stability.

## Use cases
- reconstruct 3D scenes from streaming video
- run real-time 3D reconstruction on long sequences
- estimate camera trajectory and correct drift in video
- generate dense geometry from monocular or multi-view streams
- evaluate 3D reconstruction on KITTI or Oxford Spires benchmarks
- build mapping for robotics or autonomous navigation

## When to choose
- you need feed-forward streaming 3D reconstruction rather than slow iterative optimization
- you process very long video sequences (thousands of frames) and need stable memory usage
- you want state-of-the-art reconstruction quality with real-time inference on GPU
- you need a permissively licensed (Apache-2.0) 3D foundation model with pretrained weights

## When to avoid
- you need lightweight CPU-only inference, since the model targets GPU acceleration
- you need a full production SLAM system with loop closure and sensor fusion beyond visual reconstruction
- you need a mature stable release rather than a fast-moving research codebase

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, simulation
- domain: computer-vision, artificial-intelligence, deep-learning, robotics, autonomous-vehicles
- platform: python
- tags: 3d-reconstruction, streaming-inference, foundation-model, slam, transformer, depth-estimation, point-cloud, kv-cache, gpu, linux

## Member repositories
- Robbyant/lingbot-map (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:17.014120+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:04:47.305871+00:00, confidence not recorded.
  - readme: https://github.com/Robbyant/lingbot-map (fetched 2026-08-28T04:11:17.014120+00:00, sha 4d474e646c81)
- Data as of 2026-08-30T08:39:29.467469+00:00.