---
license: apache-2.0
pipeline_tag: image-to-3d
base_model: Lucasgitguy-co/forma3d-tiny-2
base_model_relation: finetune
model_name: Forma3D Tiny 3
datasets:
- codepawl/abo-glb
tags:
- pytorch
- gaussian-splatting
- mesh-proxy-supervision
- research-prototype
---
# Forma3D Tiny 3

Continued from [Lucasgitguy-co/forma3d-tiny-2](https://huggingface.co/Lucasgitguy-co/forma3d-tiny-2) at `1648582b07a0f7f9d482a8c0f4cf89e6ecd86972`.
Same 306,321-parameter architecture, 16 cubed grid, width 64, maximum 4,096 Gaussian slots.
Fresh AdamW optimizer at the start of continuation. Completed 6000 steps;
selected step 6000 by validation loss only.

## Data and scope

512 ABO objects, 2048 orthographic Gaussian-proxy images on black.
Data revision: `c7c8293fed3b7ab122ef5d7896521a187e0962d7`. Targets are isotropic mesh surface samples, compressed to
one Gaussian per occupied grid cell. Texture surface colors are sampled; full PBR rendering
and differentiable rendering are not used. No natural-photo training is claimed.
MeshAnythingV2, Idol Diffusers, SHARP, Pixal3D, Idol Turbo and Qwen3.5-4B-3D-Coder
were **not run, merged, or distilled** in this Mac-only recipe.

Objects, shared Amazon item IDs and exact mesh/image/target hashes are grouped before splitting.
Split groups: {"train": 348, "validation": 87, "test": 77}. Test data is excluded from training and checkpoint selection.
Near duplicates can remain; overlap with ancestor training datasets is not fully known.

## Evaluation

Both checkpoints were evaluated on the identical held-out ABO proxy views.

| Metric | Starting v2 | Selected v3 |
|---|---:|---:|
| Mean test loss | 0.690551 | 0.544487 |
| Mean view occupancy IoU, threshold 0.5 | 0.286866 | 0.339860 |

These measurements do not establish natural-photo reconstruction, photorealism, mesh quality,
novel-view rendering quality or improvement on v2's original domains. No confidence intervals
or broad generalization claim. See evaluation.json, preparation.json and split manifests.

## Use

With Python 3.11, install this repository using `pip install .`, then:

```bash
image3d generate --checkpoint model.pt --image centered-object-on-black.png --out object.ply --device mps
image3d preview --ply object.ply --out preview.png
```

Output is Gaussian PLY, not a textured GLB mesh. Inputs should match centered objects on black.
The model has no text prompt conditioning. Reproduction launcher and locked dependencies are in recipe/.

## Attribution

Publisher source and weights: Apache 2.0. Retain inherited NOTICE, THIRD_PARTY.md and ATTRIBUTIONS.json.
ABO models, images and listing text: Amazon.com and the Amazon Berkeley Objects contributors,
CC BY 4.0; converted to GLB by CodePawl. The recipe further decompresses geometry,
normalizes coordinates, samples colored surfaces and renders proxy images.
See [ABO dataset](https://huggingface.co/datasets/codepawl/abo-glb),
ATTRIBUTIONS.json and licenses/LICENSE-ABO-CC-BY-4.0.txt. Raw assets are not bundled.