smesh 0.0.1

Smesh – Mesh→Point-Cloud Sensor Simulator (core skeleton)

Smesh – filthy-fast mesh→point cloud simulator

This is a minimal, type-annotated implementation of the critical core components for the Smesh simulator:

• MeshScene (mesh IO + attribute probing via VTK)
• Intersector (VTK OBBTree + optional Embree via trimesh[ray])
• PointBatch container
• AttributeComputer plug-ins (range, incidence, scan angle, returns, intensity, gps time, color/normal probe, beam footprint)
• LasWriter with streaming support and dynamic ExtraBytes
• Sampler orchestrator which glues everything together

Installation

Examples below use uv for reproducible runs, but a standard Python workflow works too:

python -m venv .venv
source .venv/bin/activate
pip install -e .

Installing straight from GitHub

To pull the latest main branch directly into a virtualenv:

pip install git+https://github.com/Chiark-Collective/smesh.git

For a reproducible install, pin to a tag (e.g., git+https://github.com/Chiark-Collective/smesh.git@v0.1.0).

Quick taste

import numpy as np
from smesh import MeshScene, Sampler, SamplerConfig, AutoIntersector, LasWriter
from smesh.core.intersector import RayBundle

scene = MeshScene(mesh_path="scene.ply")
# Make a tiny fan of rays from above
M = 1000
origins = np.tile(np.array([[0,0,10.0]]), (M,1))
dirs = np.tile(np.array([[0,0,-1.0]]), (M,1))
bundle = RayBundle(origins=origins, directions=dirs, max_range=100.0, multi_hit=False, meta={"gps_time": np.arange(M)*1e-3})

sampler = Sampler(scene, intersector=AutoIntersector(), cfg=SamplerConfig())
writer = LasWriter("out.las", compress=False)
stats = sampler.run_to_writer(writer, ray_batches=[bundle])
print(stats)

Note: for VTK-backed attribute probing and fast VTK intersector, install vtk. For Embree, install trimesh[ray] which pulls pyembree (and requires Intel Embree runtime).

CLI & Preview Runs

Quickly sample a scenario from YAML:

uv run smesh sample examples/configs/preview/aerial_lidar_preview.yaml

Override outputs or attributes on the fly:

uv run smesh sample examples/configs/preview/mobile_lidar_preview.yaml \
  --output scratch/mobile.las --attribute range --attribute scan_angle

Generate fresh preview point clouds and web-friendly images for the key capture modes:

uv run python scripts/render_examples.py            # preview configs (fast)
uv run python scripts/render_examples.py --full     # full-resolution configs
# or, without uv:
python -m smesh.cli sample examples/configs/preview/aerial_lidar_preview.yaml

The script writes point clouds into examples/outputs/ and PNG composites into examples/images/.

Workflow Examples

Capture	Preview	Command
Aerial LiDAR		uv run smesh sample examples/configs/preview/aerial_lidar_preview.yaml
Mobile LiDAR		uv run smesh sample examples/configs/preview/mobile_lidar_preview.yaml
Total Station		uv run smesh sample examples/configs/preview/total_station_preview.yaml
Photogrammetry		uv run smesh sample examples/configs/preview/photogrammetry_preview.yaml

Each preview config runs in under a minute on CPU-only machines, producing lightweight outputs for quick inspection. Higher fidelity counterparts live in examples/configs/ and can be regenerated by rerunning scripts/render_examples.py --full.

Output writers

• LasWriter streams directly to disk batch-by-batch (compression optional).
• PlyWriter and NpzWriter buffer incoming batches in memory and emit a single file when close() is called. Call close() explicitly if you manage the writer yourself.

Attribute pipeline notes

The sampler now orders attribute computers based on their declared dependencies. Custom attribute chains still work—dependencies like range_m for intensity will be inserted automatically so long as a matching producer exists.