--- license: other license_name: shapenet-license license_link: https://shapenet.org/terms task_categories: - other tags: - 3d - sdf - signed-distance-field - shapenet - point-cloud - neural-fields - implicit-neural-representation pretty_name: ShapeNetSDF size_categories: - 100B/ # 50 category configs (chair, table, airplane, ...) │ ├── train-NNNNN-of-NNNNN.parquet # rows: model_id, uniform, surface, groundtruth │ ├── val-NNNNN-of-NNNNN.parquet │ └── test-NNNNN-of-NNNNN.parquet ├── meta/ # lightweight metadata (split id lists, labels, subsets) │ ├── / # per-category split id lists │ │ └── train.txt / val.txt / test.txt │ └── all/ # global splits + curated subsets + labels │ ├── train.txt / val.txt / test.txt # global splits (all categories) │ ├── 10k10c.txt / 5k10c.txt / 5k5c.txt / 100_5c.txt # curated subsets (model-id lists) │ └── labels.json # category ↔ model_id mappings └── README.md ``` - Each Parquet **row** has columns `model_id` (string) and `uniform` / `surface` / `groundtruth`, each an `Array2D[262144, 4]` float32 tensor (`[x, y, z, sdf]` per point — see *File format* below). - The `all` config globs every category's Parquet, so `load_dataset(..., "all")` streams all 50 categories together. The split id lists under `meta/` (and curated subsets) remain for selecting specific model ids. - **50 categories**: `airplane, bag, basket, bathtub, bed, bench, birdhouse, bookshelf, bottle, bowl, bus, cabinet, camera, can, cap, car, chair, dishwasher, display, earphone, faucet, file_cabinet, guitar, jar, keyboard, knife, lamp, laptop, loudspeaker, mailbox, microphone, motorbike, mug, piano, pillow, pistol, printer, remote, rifle, rocket, skateboard, sofa, stove, table, telephone, tower, train, trash_bin, washer, watercraft` (plus the aggregated `all/` folder). - **Total size**: ~512 GB. ### File format Each point set (`uniform` / `surface` / `groundtruth`) is a `float32` array of shape `[262144, 4]`: | column | meaning | range | | ------ | ------- | ----- | | 0 (`x`) | x coordinate | `[-1, 1]` | | 1 (`y`) | y coordinate | `[-1, 1]` | | 2 (`z`) | z coordinate | `[-1, 1]` | | 3 (`sdf`) | signed distance to the surface | negative inside, positive outside | - **`uniform`** — `N = 64³ = 262,144` points sampled uniformly in `[-1, 1]³`, with their exact signed distance. Use these to supervise the field in free space. - **`surface`** — points sampled on the mesh surface with a small Gaussian perturbation (noise scale ≈ `0.02`), so SDF values are close to (but not exactly) zero. Intended for training near the surface. - **`groundtruth`** — exact on-surface points (SDF ≈ `0`), with no noise. Intended for evaluation (e.g. surface reconstruction / Chamfer / IoU). ### Splits Each category config has `train` / `val` / `test` splits (~80% / 10% / 10%, seed `42`), encoded as the Parquet partitions. Plain-text `model_id` lists under `meta/` remain for global splits and curated subsets: - `meta/all/train.txt` / `val.txt` / `test.txt` — global splits over all categories (per-category lists live in `meta//`). - `meta/all/{10k10c,5k10c,5k5c,100_5c}.txt` — curated subsets (`c`, e.g. `5k5c` = 5,000 models across 5 categories) for quick experiments. Filter the loaded dataset by `model_id`. - `meta/all/labels.json` — `{"category_to_filename": {...}, "filename_to_category": {...}}` mapping each category to its model ids and vice versa. ## Usage ```python from datasets import load_dataset # One category, one split. Array2D columns decode to numpy [262144, 4] arrays. ds = load_dataset("EPFL-IVRL/ShapeNetSDF", "chair", split="train").with_format("numpy") row = ds[0] xyz, sdf = row["uniform"][:, :3], row["uniform"][:, 3] # [262144, 3], [262144] # row also has "surface" and "groundtruth", same shape, plus "model_id". # All categories together (streamed to avoid downloading everything at once): ds_all = load_dataset("EPFL-IVRL/ShapeNetSDF", "all", split="train", streaming=True) ``` Or download the Parquet files for a category locally: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="EPFL-IVRL/ShapeNetSDF", repo_type="dataset", allow_patterns=["data/chair/**", "meta/all/labels.json"], local_dir="ShapeNetSDF", ) ``` ## How it was created For each ShapeNet Core model (`models/model_normalized.obj`): 1. Convert the mesh to a watertight manifold (via `point_cloud_utils`). 2. Normalize into the unit cube centered at the origin (scale `0.98` of the max vertex distance). 3. Sample `uniform`, `surface` (+noise), and `groundtruth` point sets. 4. Compute the signed distance for every point and save as `[N, 4]` `float32`. Processing is deterministic (per-`model_id` seeding), so results are reproducible. See `neural_field/scripts/process_shapenet_to_sdf.py` for the full pipeline and configuration. ## License & citation This dataset is created as part of the CVPR 2026 Paper "Weight Space Representation Learning via Neural Field Adaptation". ```bibtex @inproceedings{yang2026wsr, title = {Weight Space Representation Learning via Neural Field Adaptation}, author = {Yang, Zhuoqian and Salzmann, Mathieu and S{\"u}sstrunk, Sabine}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026} } ``` This dataset is derived from **ShapeNet** and is subject to the [ShapeNet terms of use](https://shapenet.org/terms). Use it for non-commercial research only, and cite ShapeNet: ```bibtex @article{chang2015shapenet, title = {ShapeNet: An Information-Rich 3D Model Repository}, author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher}, journal = {arXiv preprint arXiv:1512.03012}, year = {2015} } ```