---
license: apache-2.0
tags:
  - 3d
  - mesh-generation
  - vq-vae
  - codebook
  - topology
  - graph-neural-network
  - research
datasets:
  - allenai/objaverse
library_name: pytorch
pipeline_tag: other
---

# MeshLex Research

<div align="center">

**MeshLex: Learning a Topology-aware Patch Vocabulary for Compositional Mesh Generation**

<a href="https://github.com/Pthahnix/MeshLex-Research"><img alt="GitHub"
  src="https://img.shields.io/badge/GitHub-MeshLex--Research-181717?logo=github&logoColor=white"/></a>
<a href="https://huggingface.co/Pthahnix/MeshLex-Research"><img alt="Hugging Face"
  src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-MeshLex--Research-ffc107?color=ffc107&logoColor=white"/></a>
<a href="https://github.com/Pthahnix/MeshLex-Research/blob/main/LICENSE"><img alt="License"
  src="https://img.shields.io/badge/License-Apache_2.0-f5de53?&color=f5de53"/></a>

</div>

<hr>

## Table of Contents

1. [Overview](#overview)
2. [Current Status](#current-status)
3. [Repo Contents](#repo-contents)
4. [Core Hypothesis](#core-hypothesis)
5. [Model Architecture](#model-architecture)
6. [Experimental Results](#experimental-results)
7. [Data](#data)
8. [Quick Start](#quick-start)
9. [Timeline](#timeline)
10. [License](#license)

## Overview

A research project exploring whether 3D triangle meshes possess a finite, reusable "vocabulary" of local topological patterns — analogous to how BPE tokens form a vocabulary for natural language.

Instead of generating meshes face-by-face, MeshLex learns a **codebook of ~4096 topology-aware patches** (each covering 20-50 faces) and generates meshes by selecting, deforming, and assembling patches from this codebook. A 4000-face mesh becomes ~130 tokens — an order of magnitude more compact than the state-of-the-art (FACE, ICML 2026: ~400 tokens).

| | MeshMosaic | FreeMesh | FACE | **MeshLex** |
|---|---|---|---|---|
| Approach | Divide-and-conquer | BPE on coordinates | One-face-one-token | **Topology patch codebook** |
| Still per-face generation? | Yes | Yes | Yes | **No** |
| Has codebook? | No | Yes (coordinate-level) | No | **Yes (topology-level)** |
| Compression (4K faces) | N/A | ~300 tokens | ~400 tokens | **~130 tokens** |

## Current Status

**Feasibility validation COMPLETE — 4/4 experiments STRONG GO. Ready for formal experiment design.**

| # | Experiment | Status | Result |
|---|-----------|--------|--------|
| 1 | A-stage × 5-Category | **Done** | STRONG GO (ratio 1.145x, util 46%) |
| 2 | A-stage × LVIS-Wide | **Done** | **STRONG GO (ratio 1.019x, util 95.3%)** |
| 3 | B-stage × 5-Category | **Done** | STRONG GO (ratio 1.185x, util 47%) |
| 4 | B-stage × LVIS-Wide | **Done** | **STRONG GO (ratio 1.019x, util 94.9%)** |

Key findings:
- **More categories = dramatically better generalization**: LVIS-Wide (1156 cat) ratio 1.019x vs 5-cat 1.145x, util 95% vs 46%
- **Best result (Exp4)**: Same-cat CD 211.6, Cross-cat CD 215.8 — near-zero generalization gap
- SimVQ collapse fix successful: utilization 0.46% → 99%+ (217x improvement)
- B-stage multi-token KV decoder effective: reconstruction CD reduced 6.2%

## Repo Contents

This HuggingFace repo stores **checkpoints** and **processed datasets** for reproducibility.

### Checkpoints

| Experiment | Path | Description |
|------------|------|-------------|
| Exp1 A-stage × 5cat | `checkpoints/exp1_A_5cat/` | `checkpoint_final.pt` + `training_history.json` |
| Exp2 A-stage × LVIS-Wide | `checkpoints/exp2_A_lvis_wide/` | `checkpoint_final.pt` + `training_history.json` |
| Exp3 B-stage × 5cat | `checkpoints/exp3_B_5cat/` | `checkpoint_final.pt` + `training_history.json` |
| Exp4 B-stage × LVIS-Wide | `checkpoints/exp4_B_lvis_wide/` | `checkpoint_final.pt` + `training_history.json` |

### Data

| File / Directory | Size | Contents |
|------------------|------|----------|
| `data/meshlex_data.tar.gz` | ~1.2 GB | All processed data in one archive (recommended) |
| `data/patches/` | ~1.1 GB | NPZ patch files (5cat + LVIS-Wide splits) |
| `data/meshes/` | ~931 MB | Preprocessed decimated OBJ files (5,497 meshes) |
| `data/objaverse/` | ~2 MB | Download manifests |

The `tar.gz` archive contains patches, meshes, and manifests — download it and extract to skip all preprocessing.

## Core Hypothesis

> Mesh local topology is low-entropy and universal across object categories. A finite codebook of ~4096 topology prototypes, combined with continuous deformation parameters, can reconstruct arbitrary meshes with high fidelity.

## Model Architecture

The full model is a **VQ-VAE** with three modules:

```
Objaverse-LVIS GLB → Decimation (pyfqmr) → Normalize [-1,1]
    → METIS Patch Segmentation (~35 faces/patch)
    → PCA-aligned local coordinates
    → Face features (15-dim: vertices + normal + angles)
    → SAGEConv GNN Encoder → 128-dim embedding
    → SimVQ Codebook (K=4096, learnable reparameterization)
    → Cross-attention MLP Decoder → Reconstructed vertices
```

- **PatchEncoder**: 4-layer SAGEConv GNN + global mean pooling → 128-dim **z**
- **SimVQ Codebook**: Frozen base **C** + learnable linear **W**, effective codebook **CW = W(C)**. All 4096 entries share W's gradient — no code is ever forgotten
- **PatchDecoder**: Cross-attention with learnable vertex queries → per-vertex xyz coordinates
- **A-stage**: Single KV token decoder (baseline)
- **B-stage**: 4 KV tokens decoder (improved reconstruction, resumed from A-stage)

## Experimental Results

| Experiment | Scale | Stage | CD Ratio | Util (same) | Util (cross) | Decision |
|------------|-------|-------|----------|-------------|--------------|----------|
| Exp1 | 5 categories | A (1 KV token) | 1.145x | 46.0% | 47.0% | ✅ STRONG GO |
| Exp3 | 5 categories | B (4 KV tokens) | 1.185x | 47.1% | 47.3% | ✅ STRONG GO |
| Exp2 | 1156 categories | A (1 KV token) | **1.019x** | **95.3%** | **83.6%** | ✅ **STRONG GO** |
| **Exp4** | **1156 categories** | **B (4 KV tokens)** | **1.019x** | **94.9%** | **82.8%** | ✅ **STRONG GO** |

**CD Ratio** = Cross-category CD / Same-category CD. Closer to 1.0 = better generalization. Target: < 1.2x.

Scaling from 5 to 1156 categories causes CD ratio to **drop from 1.145x to 1.019x** (near-perfect generalization) and utilization to **surge from 46% to 95%** (nearly full codebook activation).

## Data

Training data sourced from [Objaverse-LVIS](https://huggingface.co/datasets/allenai/objaverse) (Allen AI).

- **5-Category**: chair, table, airplane, car, lamp — used for initial validation
- **LVIS-Wide**: 1156 categories from Objaverse-LVIS, 10 objects per category
  - `seen_train`: 188,696 patches (1046 categories)
  - `seen_test`: 45,441 patches (same 1046 categories, held-out objects)
  - `unseen`: 12,655 patches (110 held-out categories, never seen during training)

## Quick Start

```bash
# Clone the code repo
git clone https://github.com/Pthahnix/MeshLex-Research.git
cd MeshLex-Research

# Install dependencies
pip install -r requirements.txt
pip install torch-geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv \
    -f https://data.pyg.org/whl/torch-2.4.0+cu124.html

# Download processed data from this HF repo
pip install huggingface_hub
python -c "
from huggingface_hub import hf_hub_download
hf_hub_download('Pthahnix/MeshLex-Research', 'data/meshlex_data.tar.gz', repo_type='model', local_dir='.')
"
tar xzf data/meshlex_data.tar.gz -C data/

# Download checkpoints
python -c "
from huggingface_hub import snapshot_download
snapshot_download('Pthahnix/MeshLex-Research', allow_patterns='checkpoints/*', repo_type='model', local_dir='.')
"
mv checkpoints data/checkpoints

# Run evaluation on Exp4 (best model)
PYTHONPATH=. python scripts/evaluate.py \
  --checkpoint data/checkpoints/exp4_B_lvis_wide/checkpoint_final.pt \
  --same_cat_dirs data/patches/lvis_wide/seen_test \
  --cross_cat_dirs data/patches/lvis_wide/unseen \
  --output results/eval_results.json

# Run unit tests
python -m pytest tests/ -v
```

## Timeline

- **Day 1 (2026-03-06)**: Project inception, gap analysis, idea generation, experiment design
- **Day 2 (2026-03-07)**: Full codebase implementation (14 tasks), unit tests, initial experiment
- **Day 3 (2026-03-08)**: Diagnosed codebook collapse, fixed SimVQ, Exp1 — **STRONG GO**
- **Day 4 (2026-03-09)**: Exp2 + Exp3 completed — **STRONG GO**. Key finding: more categories = better generalization
- **Day 5 (2026-03-13)**: Pod reset recovery, expanded LVIS-Wide (1156 cat), retrained Exp2, trained Exp4 — all **STRONG GO**
- **Day 6 (2026-03-14)**: Final comparison report + visualizations. Full dataset + checkpoints backed up to HuggingFace

## License

Apache-2.0