LightweightMR — Mesh from Point Cloud (Beginner Guide)

TL;DR — Give it a .ply / .pcd / .xyz file full of 3D points, and it spits out a nice triangle mesh (.ply or .obj).

Only depends on PyTorch + NumPy + SciPy. No CUDA compiling, no Open3D, no CGAL.

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🧭 What does this actually do?

Imagine you have a laser scan of a statue — millions of dots floating in space. This code turns those dots into a solid surface made of triangles.

It does this in two stages:

Point Cloud          Stage 1: Learn SDF           Stage 2: Mesh
(just dots)    →   (learn a "distance field")  →  (triangles!)

Stage 1 — Learning a Distance Field (SDF)

The code trains a small neural network to answer:

"For any random 3D point, how far is it from the surface, and which side is it on?"

Positive = outside, negative = inside, zero = exactly on the surface.

It learns this purely from your point cloud — no camera images, no manual labels.

Stage 2 — Building the Mesh

Now that the network knows inside vs. outside, the code:

1. Sprinkles candidate vertices near the surface
2. Uses another tiny network to nudge them onto high-detail areas (curvature)
3. Projects them exactly onto the zero-distance surface
4. Builds a 3D Delaunay triangulation (like connecting dots with tetrahedra)
5. Labels each tetrahedron as "inside" or "outside"
6. The walls between inside/outside are your surface → extracted as triangles
7. Cleans up non-manifold edges by adding midpoints

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🚀 Quick Start (5 minutes)

1. Install

pip install torch numpy scipy

That's it. No C++ compilers, no 2 GB dependencies.

2. Try on a synthetic sphere (no data needed)

We included a tiny script that makes a fake point cloud so you can see it work immediately:

# Download / clone the repo files, then:
python example/make_sphere.py        # creates example/sphere.ply (3000 points)
python -m lightweightmr -i example/sphere.ply -o example/sphere_mesh.ply --device cpu

The second command will:

• Print progress bars for SDF training (~20k steps)
• Print progress bars for vertex generation (~8k steps)
• Save example/sphere_mesh.ply

On CPU this takes ~20–40 minutes. On a CUDA GPU (--device cuda) it's ~2–4 minutes.

3. Use your own scan

python -m lightweightmr -i myscan.ply -o mymesh.ply --device cpu

Supported inputs: .ply (ASCII or binary), .pcd, .xyz

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📂 What files do I need?

You only need the lightweightmr/ folder (9 Python files). Nothing else.

lightweightmr/
  __init__.py          # package marker
  __main__.py          # CLI (the command you run)
  optimize.py          # the two-stage runner (Stage 1 + Stage 2)
  sdfnet.py            # neural network for distance field
  vgnet.py             # neural network for vertex placement
  losses.py            # math that teaches the networks
  meshing.py           # Delaunay + surface extraction
  embedder.py          # positional encoding (helps the networks)
  io_utils.py          # loading PLY/PCD/XYZ, saving meshes

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⚙️ CLI Options Explained

Flag	Default	What it means
-i / --input	required	Your point cloud file
-o / --output	required	Output mesh file (.ply or .obj)
--device	cpu	cpu or cuda. GPU is much faster.
--sdf-iters	20000	How long to train the distance field. More = better quality on noisy scans.
--vg-iters	8000	How long to train vertex placement.
--vertices	3400	Target number of vertices in final mesh. More = finer detail, slower.
--k-samples	21	Samples per tetrahedron when labeling inside/outside. Higher = cleaner mesh, slower.
--save-freq	2000	Save a checkpoint every N iterations (so you can resume).
--resume-sdf	—	Path to a .pth checkpoint to skip Stage 1.

Common recipes

Fast preview (lower quality):

python -m lightweightmr -i scan.ply -o mesh.ply --sdf-iters 5000 --vg-iters 2000 --vertices 800

High quality (slower):

python -m lightweightmr -i scan.ply -o mesh.ply --sdf-iters 40000 --vg-iters 12000 --vertices 10000

Resume after Stage 1 crash:

python -m lightweightmr -i scan.ply -o mesh.ply --resume-sdf output/sdf_checkpoints/sdf_final.pth

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🐍 Python API (for scripts)

If you want to call it from your own code instead of the command line:

from lightweightmr.optimize import Runner

runner = Runner(
    pointcloud_path="myscan.ply",
    out_dir="./output",
    device="cpu",          # or "cuda"
    sdf_iters=20_000,
    vg_iters=8_000,
    vertices_size=3_400,
)

# Run both stages
vertices, faces = runner.run(mesh_path="mymesh.ply")

# Or run stages separately:
runner.train_sdf()                       # Stage 1
verts = runner.train_vg()                # Stage 2
v, f = runner.generate_mesh(verts, save_path="mymesh.ply")

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🧪 Understanding the Output

After running, you'll see a new folder ./output/ with:

output/
  sdf_checkpoints/
    sdf_final.pth          # trained distance field (can resume from this)

And your chosen output file (-o mesh.ply) contains the mesh.

You can view .ply meshes with:

• Blender (free, drag & drop)
• MeshLab (free)
• Windows 3D Viewer

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🛠️ Troubleshooting

Problem	Likely cause	Fix
Takes forever	CPU training	Use --device cuda if you have a GPU
Output mesh has holes	Not enough vertices	Increase --vertices
Noisy / wobbly mesh	Noisy input + too few SDF iters	Increase --sdf-iters to 30000+
ModuleNotFoundError	Missing dependency	pip install torch numpy scipy
ValueError on .ply	Binary PLY variant we don't parse	Convert to ASCII PLY in MeshLab/Blender

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📖 How is this different from the original paper?

The original CVPR 2025 code is powerful but heavy — it needs:

• CUDA-compiled hash encoders
• CGAL (C++ geometry library)
• Open3D, torch_scatter, spconv, fpsample, mcubes, trimesh

This reimplementation replaces all of that with pure Python + PyTorch + SciPy:

Original	This version
CUDA hash grid	Positional encoding (slower but no compile)
PointTransformerV3 vertex generator	Simple MLP (faster, no extra deps)
CGAL Delaunay + meshing	SciPy Delaunay + our own surface extractor
C++ KDTree	SciPy KDTree

Trade-off: The SDF stage may need a few more iterations on very detailed scans, but the output quality is comparable for most shapes.

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📚 Citation

If you use this, cite the original paper:

@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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License: MIT (reimplementation). Original paper and code © authors.

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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.