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LightweightMR — Pure-Python Mesh Reconstruction

Pure-Python reimplementation of "High-Fidelity Lightweight Mesh Reconstruction from Point Clouds" (CVPR 2025 Highlight, Zhang et al.)

Input: PLY / PCD / XYZ point cloud
Output: Triangle mesh (PLY / OBJ)

Quick Start

# Install
pip install torch numpy scipy

# Run
python -m lightweightmr -i myscan.ply -o mesh.ply

Or use the Python API:

from lightweightmr.optimize import Runner

runner = Runner("myscan.ply", out_dir="./output", device="cpu")
v, f = runner.run(mesh_path="mesh.ply")
print(f"Mesh: {len(v)} vertices, {len(f)} faces")

Two-Stage Pipeline

SDF Learning — Train a coordinate MLP with positional encoding to fit an implicit signed distance field to the point cloud.
Vertex Generation + Delaunay Meshing —
- Sample surface queries using SDF gradient projection.
- Train a vertex generator (MLP-only, no PointTransformerV3) to displace initial FPS samples.
- Move vertices to the learned SDF surface.
- Build a 3D Delaunay triangulation (scipy.spatial.Delaunay).
- Label tetrahedra as inside/outside by sampling the SDF.
- Extract the surface as facets between differently-labeled cells.
- Post-process with midpoint-vertex insertion to fix non-manifold edges.

Differences from Original

Feature	Original	This Reimplementation
Hash encoding	CUDA hash grid + triplane	Positional encoding only (no CUDA compilation)
Vertex generator	PointTransformerV3 + MLP	MLP-only (faster, no `spconv`/`torch_scatter`)
KDTree	C++ libkdtree	`scipy.spatial.KDTree`
Delaunay meshing	CGAL C++ binary	`scipy.spatial.Delaunay`
Mesh extraction	CGAL `create_mesh`	Pure Python facet extraction
Dependencies	Open3D, CGAL, boost, `fpsample`, `mcubes`, `trimesh`, `torch_scatter`, `spconv`	Only torch, numpy, scipy

Trade-off: Without the original hash encoding, the SDF stage converges slightly slower and may need a few more iterations on highly detailed scans. The meshing quality is comparable for typical genus-0/1 shapes.

CLI Reference

python -m lightweightmr -i INPUT.ply -o OUTPUT.ply [options]

Options:
  --device                cpu | cuda        (default: cpu)
  --sdf-iters             20000             SDF training iterations
  --vg-iters              8000              Vertex generator iterations
  --sdf-lr                0.001
  --vg-lr                 0.001
  --sdf-batch             5000              Batch size for SDF queries
  --vertices              3400              Target vertex count
  --update-size           5                 Curriculum update steps
  --update-ratio          1.2               Vertex count growth ratio
  --k-samples             21                Interior samples per tetrahedron
  --multires              8                 Positional encoding frequencies
  --project-sdf-level     0.0               Surface SDF level
  --save-freq             2000              Checkpoint frequency
  --resume-sdf            PATH.pth          Resume from SDF checkpoint

Package Structure

lightweightmr/
  __init__.py
  embedder.py      — Positional encoding (NeRF-style)
  sdfnet.py        — SDF MLP network
  vgnet.py         — Vertex generator MLP
  losses.py        — All loss functions (Chamfer, eikonal, divergence, curvature, normals)
  meshing.py       — Delaunay + SDF labeling + surface extraction + midpoint fix
  io_utils.py      — PLY/PCD/XYZ loaders, mesh exporters, FPS, normal estimation
  optimize.py      — Two-stage Runner (SDF then VG + meshing)
  __main__.py      — CLI entry point

Tips

CPU-only is slow — SDF training on CPU with 20k iterations takes ~30–60 min depending on your machine. If you have a GPU, use --device cuda.
Vertex count — Increase --vertices for finer detail (slower meshing). Decrease for faster/cleaner low-poly results.
Noise — If your point cloud is noisy, increase --sdf-iters to 30k+ and use a small --project-sdf-level (e.g. 0.001) to pull slightly inward.
Large clouds — The code automatically subsamples to ~1/60th of input points for the SDF training set. For very large scans, reduce --queries-size.

Citation

@inproceedings{zhang2025high,
  title={High-Fidelity Lightweight Mesh Reconstruction from Point Clouds},
  author={Zhang, Chen and Wang, Wentao and Li, Ximeng and Liao, Xinyao and Su, Wanjuan and Tao, Wenbing},
  booktitle={CVPR},
  pages={11739--11748},
  year={2025}
}

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "bdck/lightweightmr"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.