Lingbot Map 3d Reconstruction
A feed-forward 3D reconstruction model that processes streaming video or image sequences into point clouds at around 20 FPS. Uses a Geometric Context Transformer with paged KV cache attention to handle sequences over 10,000 frames without choking on memory. The CLI is straightforward: point it at images or video, get an interactive 3D viewer in your browser. Sky masking for outdoor scenes is a nice touch since sky points are usually garbage. Keyframe intervals and windowed mode keep long sequences tractable. The streaming approach means no iterative optimization loops, which is the main selling point here. Built for real reconstruction work, not just demos, though the example scenes suggest it handles both tourist snapshots and structured captures reasonably well.
- skills.sh installs
- 310
- GitHub stars
- 26
- last updated
- Jun 3, 2026
Install
$ npx -y skills add aradotso/trending-skills --skill lingbot-map-3d-reconstruction --agent claude-codeInstalls into .claude/skills of the current project.
Files
LingBot-Map 3D Reconstruction Skill
Skill by ara.so — Daily 2026 Skills collection.
LingBot-Map is a feed-forward 3D foundation model that reconstructs scenes from streaming image or video data using a Geometric Context Transformer. It achieves ~20 FPS on 518×378 resolution over sequences exceeding 10,000 frames via paged KV cache attention.
What It Does
- Streaming 3D reconstruction from image sequences or video
- Feed-forward inference (no iterative optimization needed)
- Outputs: point clouds with per-point confidence, camera poses, depth maps
- Key features: anchor context, pose-reference window, trajectory memory for drift correction
Installation
# 1. Create environment
conda create -n lingbot-map python=3.10 -y
conda activate lingbot-map
# 2. Install PyTorch (CUDA 12.8)
pip install torch==2.9.1 torchvision==0.24.1 --index-url https://download.pytorch.org/whl/cu128
# 3. Install lingbot-map
git clone https://github.com/Robbyant/lingbot-map.git
cd lingbot-map
pip install -e .
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- Repository
- github.com/aradotso/trending-skills
- First seen
- Jun 3, 2026
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