| --- |
| 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<n<1T |
| configs: |
| - config_name: airplane |
| data_files: |
| - split: train |
| path: data/airplane/train-*.parquet |
| - split: val |
| path: data/airplane/val-*.parquet |
| - split: test |
| path: data/airplane/test-*.parquet |
| - config_name: bag |
| data_files: |
| - split: train |
| path: data/bag/train-*.parquet |
| - split: val |
| path: data/bag/val-*.parquet |
| - split: test |
| path: data/bag/test-*.parquet |
| - config_name: basket |
| data_files: |
| - split: train |
| path: data/basket/train-*.parquet |
| - split: val |
| path: data/basket/val-*.parquet |
| - split: test |
| path: data/basket/test-*.parquet |
| - config_name: bathtub |
| data_files: |
| - split: train |
| path: data/bathtub/train-*.parquet |
| - split: val |
| path: data/bathtub/val-*.parquet |
| - split: test |
| path: data/bathtub/test-*.parquet |
| - config_name: bed |
| data_files: |
| - split: train |
| path: data/bed/train-*.parquet |
| - split: val |
| path: data/bed/val-*.parquet |
| - split: test |
| path: data/bed/test-*.parquet |
| - config_name: bench |
| data_files: |
| - split: train |
| path: data/bench/train-*.parquet |
| - split: val |
| path: data/bench/val-*.parquet |
| - split: test |
| path: data/bench/test-*.parquet |
| - config_name: birdhouse |
| data_files: |
| - split: train |
| path: data/birdhouse/train-*.parquet |
| - split: val |
| path: data/birdhouse/val-*.parquet |
| - split: test |
| path: data/birdhouse/test-*.parquet |
| - config_name: bookshelf |
| data_files: |
| - split: train |
| path: data/bookshelf/train-*.parquet |
| - split: val |
| path: data/bookshelf/val-*.parquet |
| - split: test |
| path: data/bookshelf/test-*.parquet |
| - config_name: bottle |
| data_files: |
| - split: train |
| path: data/bottle/train-*.parquet |
| - split: val |
| path: data/bottle/val-*.parquet |
| - split: test |
| path: data/bottle/test-*.parquet |
| - config_name: bowl |
| data_files: |
| - split: train |
| path: data/bowl/train-*.parquet |
| - split: val |
| path: data/bowl/val-*.parquet |
| - split: test |
| path: data/bowl/test-*.parquet |
| - config_name: bus |
| data_files: |
| - split: train |
| path: data/bus/train-*.parquet |
| - split: val |
| path: data/bus/val-*.parquet |
| - split: test |
| path: data/bus/test-*.parquet |
| - config_name: cabinet |
| data_files: |
| - split: train |
| path: data/cabinet/train-*.parquet |
| - split: val |
| path: data/cabinet/val-*.parquet |
| - split: test |
| path: data/cabinet/test-*.parquet |
| - config_name: camera |
| data_files: |
| - split: train |
| path: data/camera/train-*.parquet |
| - split: val |
| path: data/camera/val-*.parquet |
| - split: test |
| path: data/camera/test-*.parquet |
| - config_name: can |
| data_files: |
| - split: train |
| path: data/can/train-*.parquet |
| - split: val |
| path: data/can/val-*.parquet |
| - split: test |
| path: data/can/test-*.parquet |
| - config_name: cap |
| data_files: |
| - split: train |
| path: data/cap/train-*.parquet |
| - split: val |
| path: data/cap/val-*.parquet |
| - split: test |
| path: data/cap/test-*.parquet |
| - config_name: car |
| data_files: |
| - split: train |
| path: data/car/train-*.parquet |
| - split: val |
| path: data/car/val-*.parquet |
| - split: test |
| path: data/car/test-*.parquet |
| - config_name: chair |
| data_files: |
| - split: train |
| path: data/chair/train-*.parquet |
| - split: val |
| path: data/chair/val-*.parquet |
| - split: test |
| path: data/chair/test-*.parquet |
| - config_name: dishwasher |
| data_files: |
| - split: train |
| path: data/dishwasher/train-*.parquet |
| - split: val |
| path: data/dishwasher/val-*.parquet |
| - split: test |
| path: data/dishwasher/test-*.parquet |
| - config_name: display |
| data_files: |
| - split: train |
| path: data/display/train-*.parquet |
| - split: val |
| path: data/display/val-*.parquet |
| - split: test |
| path: data/display/test-*.parquet |
| - config_name: earphone |
| data_files: |
| - split: train |
| path: data/earphone/train-*.parquet |
| - split: val |
| path: data/earphone/val-*.parquet |
| - split: test |
| path: data/earphone/test-*.parquet |
| - config_name: faucet |
| data_files: |
| - split: train |
| path: data/faucet/train-*.parquet |
| - split: val |
| path: data/faucet/val-*.parquet |
| - split: test |
| path: data/faucet/test-*.parquet |
| - config_name: file_cabinet |
| data_files: |
| - split: train |
| path: data/file_cabinet/train-*.parquet |
| - split: val |
| path: data/file_cabinet/val-*.parquet |
| - split: test |
| path: data/file_cabinet/test-*.parquet |
| - config_name: guitar |
| data_files: |
| - split: train |
| path: data/guitar/train-*.parquet |
| - split: val |
| path: data/guitar/val-*.parquet |
| - split: test |
| path: data/guitar/test-*.parquet |
| - config_name: jar |
| data_files: |
| - split: train |
| path: data/jar/train-*.parquet |
| - split: val |
| path: data/jar/val-*.parquet |
| - split: test |
| path: data/jar/test-*.parquet |
| - config_name: keyboard |
| data_files: |
| - split: train |
| path: data/keyboard/train-*.parquet |
| - split: val |
| path: data/keyboard/val-*.parquet |
| - split: test |
| path: data/keyboard/test-*.parquet |
| - config_name: knife |
| data_files: |
| - split: train |
| path: data/knife/train-*.parquet |
| - split: val |
| path: data/knife/val-*.parquet |
| - split: test |
| path: data/knife/test-*.parquet |
| - config_name: lamp |
| data_files: |
| - split: train |
| path: data/lamp/train-*.parquet |
| - split: val |
| path: data/lamp/val-*.parquet |
| - split: test |
| path: data/lamp/test-*.parquet |
| - config_name: laptop |
| data_files: |
| - split: train |
| path: data/laptop/train-*.parquet |
| - split: val |
| path: data/laptop/val-*.parquet |
| - split: test |
| path: data/laptop/test-*.parquet |
| - config_name: loudspeaker |
| data_files: |
| - split: train |
| path: data/loudspeaker/train-*.parquet |
| - split: val |
| path: data/loudspeaker/val-*.parquet |
| - split: test |
| path: data/loudspeaker/test-*.parquet |
| - config_name: mailbox |
| data_files: |
| - split: train |
| path: data/mailbox/train-*.parquet |
| - split: val |
| path: data/mailbox/val-*.parquet |
| - split: test |
| path: data/mailbox/test-*.parquet |
| - config_name: microphone |
| data_files: |
| - split: train |
| path: data/microphone/train-*.parquet |
| - split: val |
| path: data/microphone/val-*.parquet |
| - split: test |
| path: data/microphone/test-*.parquet |
| - config_name: motorbike |
| data_files: |
| - split: train |
| path: data/motorbike/train-*.parquet |
| - split: val |
| path: data/motorbike/val-*.parquet |
| - split: test |
| path: data/motorbike/test-*.parquet |
| - config_name: mug |
| data_files: |
| - split: train |
| path: data/mug/train-*.parquet |
| - split: val |
| path: data/mug/val-*.parquet |
| - split: test |
| path: data/mug/test-*.parquet |
| - config_name: piano |
| data_files: |
| - split: train |
| path: data/piano/train-*.parquet |
| - split: val |
| path: data/piano/val-*.parquet |
| - split: test |
| path: data/piano/test-*.parquet |
| - config_name: pillow |
| data_files: |
| - split: train |
| path: data/pillow/train-*.parquet |
| - split: val |
| path: data/pillow/val-*.parquet |
| - split: test |
| path: data/pillow/test-*.parquet |
| - config_name: pistol |
| data_files: |
| - split: train |
| path: data/pistol/train-*.parquet |
| - split: val |
| path: data/pistol/val-*.parquet |
| - split: test |
| path: data/pistol/test-*.parquet |
| - config_name: printer |
| data_files: |
| - split: train |
| path: data/printer/train-*.parquet |
| - split: val |
| path: data/printer/val-*.parquet |
| - split: test |
| path: data/printer/test-*.parquet |
| - config_name: remote |
| data_files: |
| - split: train |
| path: data/remote/train-*.parquet |
| - split: val |
| path: data/remote/val-*.parquet |
| - split: test |
| path: data/remote/test-*.parquet |
| - config_name: rifle |
| data_files: |
| - split: train |
| path: data/rifle/train-*.parquet |
| - split: val |
| path: data/rifle/val-*.parquet |
| - split: test |
| path: data/rifle/test-*.parquet |
| - config_name: rocket |
| data_files: |
| - split: train |
| path: data/rocket/train-*.parquet |
| - split: val |
| path: data/rocket/val-*.parquet |
| - split: test |
| path: data/rocket/test-*.parquet |
| - config_name: skateboard |
| data_files: |
| - split: train |
| path: data/skateboard/train-*.parquet |
| - split: val |
| path: data/skateboard/val-*.parquet |
| - split: test |
| path: data/skateboard/test-*.parquet |
| - config_name: sofa |
| data_files: |
| - split: train |
| path: data/sofa/train-*.parquet |
| - split: val |
| path: data/sofa/val-*.parquet |
| - split: test |
| path: data/sofa/test-*.parquet |
| - config_name: stove |
| data_files: |
| - split: train |
| path: data/stove/train-*.parquet |
| - split: val |
| path: data/stove/val-*.parquet |
| - split: test |
| path: data/stove/test-*.parquet |
| - config_name: table |
| data_files: |
| - split: train |
| path: data/table/train-*.parquet |
| - split: val |
| path: data/table/val-*.parquet |
| - split: test |
| path: data/table/test-*.parquet |
| - config_name: telephone |
| data_files: |
| - split: train |
| path: data/telephone/train-*.parquet |
| - split: val |
| path: data/telephone/val-*.parquet |
| - split: test |
| path: data/telephone/test-*.parquet |
| - config_name: tower |
| data_files: |
| - split: train |
| path: data/tower/train-*.parquet |
| - split: val |
| path: data/tower/val-*.parquet |
| - split: test |
| path: data/tower/test-*.parquet |
| - config_name: train |
| data_files: |
| - split: train |
| path: data/train/train-*.parquet |
| - split: val |
| path: data/train/val-*.parquet |
| - split: test |
| path: data/train/test-*.parquet |
| - config_name: trash_bin |
| data_files: |
| - split: train |
| path: data/trash_bin/train-*.parquet |
| - split: val |
| path: data/trash_bin/val-*.parquet |
| - split: test |
| path: data/trash_bin/test-*.parquet |
| - config_name: washer |
| data_files: |
| - split: train |
| path: data/washer/train-*.parquet |
| - split: val |
| path: data/washer/val-*.parquet |
| - split: test |
| path: data/washer/test-*.parquet |
| - config_name: watercraft |
| data_files: |
| - split: train |
| path: data/watercraft/train-*.parquet |
| - split: val |
| path: data/watercraft/val-*.parquet |
| - split: test |
| path: data/watercraft/test-*.parquet |
| - config_name: all |
| data_files: |
| - split: train |
| path: data/*/train-*.parquet |
| - split: val |
| path: data/*/val-*.parquet |
| - split: test |
| path: data/*/test-*.parquet |
| --- |
| |
| # ShapeNetSDF |
|
|
| Signed Distance Field (SDF) point samples derived from |
| [ShapeNet Core](https://shapenet.org), for training and evaluating implicit |
| neural representations / neural fields on 3D shapes. |
|
|
| Each shape is converted into a watertight manifold, normalized into the unit |
| cube `[-1, 1]³`, and sampled into three point sets (`uniform`, `surface`, |
| `groundtruth`), each stored as a `[N, 4]` `float32` array of `[x, y, z, sdf]`. |
|
|
| This dataset is produced by `process_shapenet_to_sdf.py` from the |
| [wsr.pytorch](https://github.com/IVRL/wsr.pytorch) neural-field codebase. |
|
|
| ## Dataset structure |
|
|
| The dataset is stored as **sharded Parquet** (one config per category, with |
| `train` / `val` / `test` splits). Each row is one shape; the three point sets |
| (`uniform`, `surface`, `groundtruth`) are stored as fixed-shape `[262144, 4]` |
| float32 tensors in their own columns. |
|
|
| ``` |
| ShapeNetSDF/ |
| ├── data/ # heavy data: sharded parquet, one folder per category |
| │ └── <category>/ # 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) |
| │ ├── <category>/ # 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/<category>/`). |
| - `meta/all/{10k10c,5k10c,5k5c,100_5c}.txt` — curated subsets |
| (`<num_models><num_categories>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} |
| } |
| ``` |
|
|
