smesh 0.0.5

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

SMesh – mesh→point cloud simulator

Sample points from a Mesh. Smesh.

Yeah, it wasn't my best day. But it sounds utterly filthy, which I enjoy.

Installation

You wouldn't guess:

pip install smesh

Optional extras for faster intersectors and LAS compression:

pip install "smesh[vtk]"      # VTK-backed attribute probing & intersector
pip install "smesh[embree]"   # Embree (via trimesh[ray])
pip install "smesh[laz]"      # LAZ compression

Workflow Examples

Capture	Preview	Command
Raw Mesh Baseline		python scripts/render_examples.py --example raw_mesh
Aerial LiDAR		smesh sample examples/configs/preview/aerial_lidar_preview.yaml
Mobile LiDAR		smesh sample examples/configs/preview/mobile_lidar_preview.yaml
Total Station		smesh sample examples/configs/preview/total_station_preview.yaml
Photogrammetry		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.

Want to help optimise it? Open a PR! See open TODOs.

One Scenario, Three Entry Points

The aerial preview (examples/configs/preview/aerial_lidar_preview.yaml) is reproduced below via config, pure CLI options, and the Python SDK.

Config file

smesh sample examples/configs/preview/aerial_lidar_preview.yaml

Does it all from a config file, which looks like:

type: lidar
mesh:
  path: ../../meshes/preview_scene.ply
trajectory:
  kind: lawnmower
  altitude_m: 40.0
  speed_mps: 20.0
  line_spacing_m: 1.2
  heading_deg: 0.0
pattern:
  kind: oscillating
  fov_deg: 110.0
  line_rate_hz: 25.0
  pulses_per_line: 600
sensor:
  kind: lidar
  max_range_m: 200.0
  multi_return: true
  beam_divergence_mrad: 0.4
  num_lines: 60
noise:
  kind: lidar
  sigma_range_m: 0.05
  sigma_angle_deg: 0.1
  keep_prob: 0.99
sampler:
  batch_size_rays: 20000
attributes:
  - range
  - incidence
  - scan_angle
  - intensity
  - returns
  - gps_time
output:
  path: aerial_preview.las
  format: las
seed: 101

CLI arguments (no YAML)

If you like passing around flags like a paid shill at a far-right rally, knock yourself out, but don't expect me to type them here:

mkdir -p scratch
smesh sample-lidar --mesh examples/meshes/preview_scene.ply \
  --output scratch/aerial_cli.las --seed 101

Use --help if you need to.

Use SDK

If you're planning to smesh stuff from another project or smesh in a larger pipeline, you can use it like a library:

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).

If you already have a YAML scenario, you can also run it directly from Python:

from smesh.sdk import sample_from_config

result = sample_from_config("examples/configs/preview/aerial_lidar_preview.yaml")
print(result.output_path, result.stats["points"])

This is built from a bunch of components that i've smeshed together mostly with vibes so don't ask me any detailed questions. Here's what the computer said:

• 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

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.